Method and apparatus for estimating phase noise in wireless communication system

ABSTRACT

A method for estimating a phase noise by a wireless device in a wireless communication system according to an embodiment of the present disclosure comprises the steps of: receiving a pre-configured pilot signal and a phase tracking reference signal (PTRS); calculating a first phase noise on the basis of the pre-configured pilot signal; calculating a common phase error (CPE) on the basis of the phase tracking reference signal (PTRS); and estimating a phase noise (PN) on the basis of the first phase noise and the common phase error (CPE). The PN is estimated through interpolation based on a specific reference point, and the specific reference point is based on the first phase noise and the CPE.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is the National Stage filing under 35 U.S.C. 371 ofInternational Application No. PCT/KR2020/009136, filed on Jul. 10, 2020,the contents of which are all hereby incorporated by reference herein intheir entirety.

TECHNICAL FIELD

The present disclosure relates to a method and an apparatus forestimating phase noise in a wireless communication system.

BACKGROUND

A mobile communication system was developed to provide a voice servicewhile ensuring the activity of a user. However, the area of the mobilecommunication system has extended up to data services in addition tovoice. Due to a current explosive increase in traffic, there is ashortage of resources. Accordingly, there is a need for a more advancedmobile communication system because users demand higher speed services.

Requirements for a next-generation mobile communication system need toable to support the accommodation of explosive data traffic, a dramaticincrease in the data rate per user, the accommodation of a significantincrease in the number of connected devices, very low end-to-endlatency, and high-energy efficiency. To this end, various technologies,such as dual connectivity, massive multiple input multiple output(MIMO), in-band full duplex, non-orthogonal multiple access (NOMA), thesupport of a super wideband, and device networking, are researched.

SUMMARY

The present disclosure provides a method and an apparatus for estimatingphase noise.

In 5G NR, a method of removing a Common Phase Error (CPE) of a PN usinga pilot signal called a Phase Tracking Reference Signal (PTRS) isutilized. The phase noise estimation method using the CPE showssatisfactory performance when a change in phase noise (PN) in one OFDMsymbol is small. However, when a change in phase noise in one OFDMsymbol increases due to an increase in frequency, the performance of thephase noise estimation method is limited (i.e., the accuracy of phasenoise estimation decreases).

To compensate for this, a method for estimating a PN in a form similarto an actual PN value through linear interpolation at the center of asymbol based on a CPE value is being used. However, the above method hasproblems in that 1) there is a difference between the CPE value and theactual PN value at the center point of the symbol as a reference, and 2)the characteristic of phase noise is not considered because there is nocriterion for the interpolation. The performance enhancement of phasenoise estimation according to the above method is not significant.

Accordingly, the present disclosure provides a method and an apparatusfor estimating a phase noise capable of solving the above-describedproblems of the prior art.

The technical objects of the present disclosure are not limited to theaforementioned technical objects, and other technical objects, which arenot mentioned above, will be apparently appreciated by a person havingordinary skill in the art from the following description.

Technical Solution

A method for estimating a phase noise by a wireless device in a wirelesscommunication system according to an embodiment of the presentdisclosure includes: receiving a pre-configured pilot signal and a phasetracking reference signal (PTRS); calculating first phase noise based onthe pre-configured pilot signal; calculating a common phase error (CPE)based on the phase tracking reference signal (PTRS); and estimatingphase noise (PN) based on the first phase noise and the common phaseerror (CPE).

The PN is estimated through interpolation based on a specific referencepoint, and the specific reference point is based on the first phasenoise and the CPE.

The pre-configured pilot signal may be transmitted in one region of atime region allocated for a cyclic prefix (CP) of the PTRS.

The one region may be positioned at a frontmost portion of the timeregion allocated for the CP.

The pre-configured pilot signal may be transmitted in a specific timeregion and the specific time region may be positioned before the timeregion allocated for the CP of the PTRS.

The first phase noise may be based on a mean of phase noise during atime duration for which the pre-configured pilot signal is transmitted.

At least one time function related to the PN may be determined based onthe specific reference point and the first phase noise, and a mean ofintegral values based on the at least one time function may be equal tothe CPE.

The specific reference point may satisfy the following equation,

PN _(m)(q)=2CPE _(PTRS)(q)−½PN _(est)(q)−½PN _(est)(q+1)

Here, q may represent a symbol index, PN_(m) may represent the specificreference point, CPE_(PTRS) may represent the CPE, and PN_(est) mayrepresent the first phase noise.

A wireless device for estimating phase noise in a wireless communicationsystem according to another embodiment of the present disclosureincludes: one or more transceivers; one or more processors controllingthe one or more transceivers; and one or more memories operativelyconnectable to the one or more processors, and storing instructions ofperforming operations when the estimation of the phase noise is executedby the one or more processors.

The operations include receiving a pre-configured pilot signal and aphase tracking reference signal (PTRS), calculating first phase noisebased on the pre-configured pilot signal, calculating a common phaseerror (CPE) based on the phase tracking reference signal (PTRS), andestimating phase noise (PN) based on the first phase noise and thecommon phase error (CPE).

The PN is estimated through interpolation based on a specific referencepoint, and the specific reference point is based on the first phasenoise and the CPE.

The pre-configured pilot signal may be transmitted in one region of atime region allocated for a cyclic prefix (CP) of the PTRS.

The pre-configured pilot signal may be transmitted in a specific timeregion and the specific time region may be positioned before the timeregion allocated for the CP of the PTRS.

The first phase noise may be based on a mean of phase noise during atime duration for which the pre-configured pilot signal is transmitted.

At least one time function related to the PN may be determined based onthe specific reference point and the first phase noise, and a mean ofintegral values based on the at least one time function may be equal tothe CPE.

The specific reference point may satisfy the following equation,

PN _(m)(q)=2CPE _(PTRS)(q)−½PN _(est)(q)−½PN _(est)(q+1)

Here, q may represent a symbol index, PN_(m) may represent the specificreference point, CPE_(PTRS) may represent the CPE, and PN_(est) mayrepresent the first phase noise.

An apparatus according to yet another embodiment of the presentdisclosure includes: one or more memories and one or more processorsfunctionally connected to the one or more memories.

The one or more processors are configured to control the apparatus toreceive a pre-configured pilot signal and a phase tracking referencesignal (PTRS), calculate first phase noise based on the pre-configuredpilot signal, calculate a common phase error (CPE) based on the phasetracking reference signal (PTRS), and estimate phase noise (PN) based onthe first phase noise and the common phase error (CPE).

The PN is estimated through interpolation based on a specific referencepoint, and the specific reference point is based on the first phasenoise and the CPE.

In still yet another aspect, one or more non-transitorycomputer-readable media store one or more instructions.

The one or more instructions executable by one or more processors areconfigured to instruct an apparatus to receive a pre-configured pilotsignal and a phase tracking reference signal (PTRS), calculate firstphase noise based on the pre-configured pilot signal, calculate a commonphase error (CPE) based on the phase tracking reference signal (PTRS),and estimate phase noise (PN) based on the first phase noise and thecommon phase error (CPE).

The PN is estimated through interpolation based on a specific referencepoint, and the specific reference point is based on the first phasenoise and the CPE.

Since the common phase error (CPE) cannot be used as a valuerepresenting a PN value at a specific time, the CPE causes many errorsas a reference point for interpolation.

According to an embodiment of the present disclosure, a first phasenoise is calculated in a time region from a preset pilot signal, and thecommon phase error (CPE) is calculated from a phase tracking referencesignal (PTRS) and phase noise (PN) is estimated through interpolationbased on a specific reference point. The specific reference point isbased on the first phase noise and the CPE. Therefore, interpolation forestimating phase noise is performed based on the specific referencepoint that can indicate a PN value at a specific time, so thatestimation performance can be further improved.

In order to determine the specific reference point, the preconfiguredpilot signal must be transmitted in addition to the phase trackingreference signal (PTRS). According to an embodiment of the presentdisclosure, the preconfigured pilot signal is transmitted in one regionof a time region allocated for a cyclic prefix (CP) of the PTRS, and theone region is positioned at a frontmost portion of the time regionallocated for the CP. Therefore, it is possible to minimize InterSymbolInterference (ISI) and system change caused by adding a new pilot to anexisting PTRS pilot.

Effects which may be obtained from the present disclosure are notlimited by the above effects, and other effects that have not beenmentioned may be clearly understood from the above description by thoseskilled in the art to which the present disclosure pertains.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates physical channels and general signal transmissionused in a 3GPP system.

FIG. 2 is a view showing an example of a communication structureprovidable in a 6G system applicable to the present disclosure.

FIG. 3 illustrates a structure of a perceptron to which the methodproposed in the present disclosure can be applied.

FIG. 4 illustrates the structure of a multilayer perceptron to which themethod proposed in the present disclosure can be applied.

FIG. 5 illustrates a structure of a deep neural network to which themethod proposed in the present disclosure can be applied.

FIG. 6 illustrates the structure of a convolutional neural network towhich the method proposed in the present disclosure can be applied.

FIG. 7 illustrates a filter operation in a convolutional neural networkto which the method proposed in the present disclosure can be applied.

FIG. 8 illustrates a neural network structure in which a circular loopto which the method proposed in the present disclosure can be applied.

FIG. 9 illustrates an operation structure of a recurrent neural networkto which the method proposed in the present disclosure can be applied.

FIG. 10 is a view showing an electromagnetic spectrum applicable to thepresent disclosure.

FIG. 11 is a view showing a THz communication method applicable to thepresent disclosure.

FIG. 12 is a view showing a THz wireless communication transceiverapplicable to the present disclosure.

FIG. 13 is a view showing a THz signal generation method applicable tothe present disclosure.

FIG. 14 is a view showing a wireless communication transceiverapplicable to the present disclosure.

FIG. 15 is a view showing a transmitter structure based on a photonicsource applicable to the present disclosure.

FIG. 16 is a view showing an optical modulator structure applicable tothe present disclosure.

FIG. 17 is a flowchart illustrating an example of a DL PTRS procedure.

FIG. 18 is a graph for describing estimation of a phase noise using acommon phase error (CPE) according to the prior art.

FIG. 19 is a view for explaining a pilot structure according to anembodiment of the present disclosure by comparing with a conventionalscheme.

FIG. 20 illustrates a structure of a pilot according to an embodiment ofthe present disclosure.

FIG. 21 is a block diagram for describing a method for estimating aphase noise according to an embodiment of the present disclosure.

FIG. 22 is a graph for describing a reference point of interpolationperformed for estimation of a phase noise according to an embodiment ofthe present disclosure.

FIG. 23 is a diagram for describing the performance of phase noiseestimation according to the method proposed in the present disclosure bycomparing with the prior art.

FIGS. 24 and 25 are diagrams for describing a block error rate (BER)when the method proposed in the present disclosure is applied bycomparing with the conventional scheme.

FIG. 26 is a flowchart for describing a method for estimating, by awireless device, a phase noise in a wireless communication systemaccording to an embodiment of the present disclosure.

FIG. 27 illustrates a communication system 1 applied to the presentdisclosure.

FIG. 28 illustrates wireless devices applicable to the presentdisclosure.

FIG. 29 illustrates a signal process circuit for a transmission signalapplied to the present disclosure.

FIG. 30 illustrates another example of a wireless device applied to thepresent disclosure.

FIG. 31 illustrates a hand-held device applied to the presentdisclosure.

DETAILED DESCRIPTION

Hereinafter, embodiments disclosed in the present disclosure will bedescribed in detail with reference to the accompanying drawings, but thesame or similar components are denoted by the same and similar referencenumerals, and redundant descriptions thereof will be omitted. Thesuffixes “module” and “unit” for components used in the followingdescription are given or used interchangeably in consideration of onlythe ease of preparation of the specification, and do not have meaningsor roles that are distinguished from each other by themselves. Inaddition, in describing the embodiments disclosed in the presentdisclosure, when it is determined that a detailed description of relatedknown technologies may obscure the subject matter of the embodimentsdisclosed in the present disclosure, the detailed description thereofwill be omitted. In addition, the accompanying drawings are for easyunderstanding of the embodiments disclosed in the present disclosure,and the technical idea disclosed in the present disclosure is notlimited by the accompanying drawings, and all modifications included inthe spirit and scope of the present disclosure, It should be understoodto include equivalents or substitutes.

In the present disclosure, a base station has the meaning of a terminalnode of a network over which the base station directly communicates witha terminal. In this document, a specific operation that is described tobe performed by a base station may be performed by an upper node of thebase station according to circumstances. That is, it is evident that ina network including a plurality of network nodes including a basestation, various operations performed for communication with a terminalmay be performed by the base station or other network nodes other thanthe base station. The base station (BS) may be substituted with anotherterm, such as a fixed station, a Node B, an eNB (evolved-NodeB), a basetransceiver system (BTS), an access point (AP), or generation NB(general NB, gNB). Furthermore, the terminal may be fixed or may havemobility and may be substituted with another term, such as userequipment (UE), a mobile station (MS), a user terminal (UT), a mobilesubscriber station (MSS), a subscriber station (SS), an advanced mobilestation (AMS), a wireless terminal (WT), a machine-type communication(MTC) device, a machine-to-Machine (M2M) device, or a device-to-device(D2D) device.

Hereinafter, downlink (DL) means communication from a base station toUE, and uplink (UL) means communication from UE to a base station. InDL, a transmitter may be part of a base station, and a receiver may bepart of UE. In UL, a transmitter may be part of UE, and a receiver maybe part of a base station.

Specific terms used in the following description have been provided tohelp understanding of the present disclosure, and the use of suchspecific terms may be changed in various forms without departing fromthe technical sprit of the present disclosure.

The following technologies may be used in a variety of wirelesscommunication systems, such as code division multiple access (CDMA),frequency division multiple access (FDMA), time division multiple access(TDMA), orthogonal frequency division multiple access (OFDMA), singlecarrier frequency division multiple access (SC-FDMA), and non-orthogonalmultiple access (NOMA). CDMA may be implemented using a radiotechnology, such as universal terrestrial radio access (UTRA) orCDMA2000. TDMA may be implemented using a radio technology, such asglobal system for mobile communications (GSM)/general packet radioservice (GPRS)/enhanced data rates for GSM evolution (EDGE). OFDMA maybe implemented using a radio technology, such as Institute of electricaland electronics engineers (IEEE) 802.11 (Wi-Fi), IEEE 802.16 (WiMAX),IEEE 802.20, or evolved UTRA (E-UTRA). UTRA is part of a universalmobile telecommunications system (UMTS). 3rd generation partnershipproject (3GPP) Long term evolution (LTE) is part of an evolved UMTS(E-UMTS) using evolved UMTS terrestrial radio access (E-UTRA), and itadopts OFDMA in downlink and adopts SC-FDMA in uplink. LTE-advanced(LTE-A) is the evolution of 3GPP LTE.

For clarity, the description is based on a 3GPP communication system(eg, LTE, NR, etc.), but the technical idea of the present disclosure isnot limited thereto. LTE refers to the technology after 3GPP TS 36.xxxRelease 8. In detail, LTE technology after 3GPP TS 36.xxx Release 10 isreferred to as LTE-A, and LTE technology after 3GPP TS 36.xxx Release 13is referred to as LTE-A pro. 3GPP NR refers to the technology after TS38.xxx Release 15. 3GPP 6G may mean technology after TS Release 17and/or Release 18. “xxx” means standard document detail number.LTE/NR/6G may be collectively referred to as a 3GPP system. Backgroundart, terms, abbreviations, and the like used in the description of thepresent disclosure may refer to matters described in standard documentspublished before the present disclosure. For example, you can refer tothe following document:

3GPP LTE

-   -   36.211: Physical channels and modulation    -   36.212: Multiplexing and channel coding    -   36.213: Physical layer procedures    -   36.300: Overall description    -   36.331: Radio Resource Control (RRC)

3GPP NR

-   -   38.211: Physical channels and modulation    -   38.212: Multiplexing and channel coding    -   38.213: Physical layer procedures for control    -   38.214: Physical layer procedures for data    -   38.300: NR and NG-RAN Overall Description    -   38.331: Radio Resource Control (RRC) protocol specification

Physical Channel and Frame Structure

Physical Channels and General Signal Transmission

FIG. 1 illustrates physical channels and general signal transmissionused in a 3GPP system. In a wireless communication system, a terminalreceives information from a base station through a downlink (DL), andthe terminal transmits information to the base station through an uplink(UL). The information transmitted and received by the base station andthe terminal includes data and various control information, and variousphysical channels exist according to the type/use of informationtransmitted and received by them.

When the terminal is powered on or newly enters a cell, the terminalperforms an initial cell search operation such as synchronizing with thebase station (S101). To this end, the UE receives a PrimarySynchronization Signal (PSS) and a Secondary Synchronization Signal(SSS) from the base station to synchronize with the base station andobtain information such as cell ID. Thereafter, the terminal may receivea physical broadcast channel (PBCH) from the base station to obtainintra-cell broadcast information. Meanwhile, the UE may receive adownlink reference signal (DL RS) in the initial cell search step tocheck a downlink channel state.

After completing the initial cell search, the UE receives a physicaldownlink control channel (PDCCH) and a physical downlink shared channel(PDSCH) according to the information carried on the PDCCH, therebyreceiving a more specific system Information can be obtained (S102).

On the other hand, when accessing the base station for the first time orwhen there is no radio resource for signal transmission, the terminalmay perform a random access procedure (RACH) for the base station (S103to S106). To this end, the UE transmits a specific sequence as apreamble through a physical random access channel (PRACH) (S103 andS105), and a response message to the preamble through a PDCCH and acorresponding PDSCH (RAR (Random Access Response) message) In the caseof contention-based RACH, a contention resolution procedure may beadditionally performed (S106).

After performing the above-described procedure, the UE receivesPDCCH/PDSCH (S107) and physical uplink shared channel (PUSCH)/physicaluplink control channel as a general uplink/downlink signal transmissionprocedure. (Physical Uplink Control Channel; PUCCH) transmission (S108)can be performed. In particular, the terminal may receive downlinkcontrol information (DCI) through the PDCCH. Here, the DCI includescontrol information such as resource allocation information for theterminal, and different formats may be applied according to the purposeof use.

On the other hand, control information transmitted by the terminal tothe base station through uplink or received by the terminal from thebase station is a downlink/uplink ACK/NACK signal, a channel qualityindicator (CQI), a precoding matrix index (PMI), and (Rank Indicator)may be included. The terminal may transmit control information such asCQI/PMI/RI described above through PUSCH and/or PUCCH.

Structure of Uplink and Downlink Channels

Downlink Channel Structure

The base station transmits a related signal to the terminal through adownlink channel to be described later, and the terminal receives arelated signal from the base station through a downlink channel to bedescribed later.

(1) Physical Downlink Shared Channel (PDSCH)

PDSCH carries downlink data (eg, DL-shared channel transport block,DL-SCH TB), and includes Quadrature Phase Shift Keying (QPSK),Quadrature Amplitude Modulation (QAM), 64 QAM, 256 QAM, etc. Themodulation method is applied. A codeword is generated by encoding TB.The PDSCH can carry multiple codewords. Scrambling and modulationmapping are performed for each codeword, and modulation symbolsgenerated from each codeword are mapped to one or more layers (Layermapping). Each layer is mapped to a resource together with ademodulation reference signal (DMRS) to generate an OFDM symbol signal,and is transmitted through a corresponding antenna port.

(2) Physical Downlink Control Channel (PDCCH)

The PDCCH carries downlink control information (DCI) and a QPSKmodulation method is applied. One PDCCH is composed of 1, 2, 4, 8, 16Control Channel Elements (CCEs) according to the Aggregation Level (AL).One CCE consists of 6 REGs (Resource Element Group). One REG is definedby one OFDM symbol and one (P)RB.

The UE acquires DCI transmitted through the PDCCH by performing decoding(aka, blind decoding) on the set of PDCCH candidates. The set of PDCCHcandidates decoded by the UE is defined as a PDCCH search space set. Thesearch space set may be a common search space or a UE-specific searchspace. The UE may acquire DCI by monitoring PDCCH candidates in one ormore search space sets set by MIB or higher layer signaling.

Uplink Channel Structure

The terminal transmits a related signal to the base station through anuplink channel to be described later, and the base station receives arelated signal from the terminal through an uplink channel to bedescribed later.

(1) Physical Uplink Shared Channel (PUSCH)

PUSCH carries uplink data (eg, UL-shared channel transport block, UL-SCHTB) and/or uplink control information (UCI), and CP-OFDM (CyclicPrefix-Orthogonal Frequency Division Multiplexing) waveform (waveform),DFT-s-OFDM (Discrete Fourier Transform-spread-Orthogonal FrequencyDivision Multiplexing) is transmitted based on the waveform. When thePUSCH is transmitted based on the DFT-s-OFDM waveform, the UE transmitsthe PUSCH by applying transform precoding. For example, when transformprecoding is not possible (eg, transform precoding is disabled), the UEtransmits a PUSCH based on the CP-OFDM waveform, and when transformprecoding is possible (eg, transform precoding is enabled), the UE isCP-OFDM. PUSCH may be transmitted based on a waveform or a DFT-s-OFDMwaveform. PUSCH transmission is dynamically scheduled by the UL grant inthe DCI or is semi-static based on higher layer (e.g., RRC) signaling(and/or Layer 1 (L1) signaling (e.g., PDCCH)). Can be scheduled(configured grant). PUSCH transmission may be performed based on acodebook or a non-codebook.

(2) Physical Uplink Control Channel (PUCCH)

The PUCCH carries uplink control information, HARQ-ACK, and/orscheduling request (SR), and may be divided into a plurality of PUCCHsaccording to the PUCCH transmission length.

6G System General

A 6G (wireless communication) system has purposes such as (i) very highdata rate per device, (ii) a very large number of connected devices,(iii) global connectivity, (iv) very low latency, (v) decrease in energyconsumption of battery-free IoT devices, (vi) ultra-reliableconnectivity, and (vii) connected intelligence with machine learningcapacity. The vision of the 6G system may include four aspects such as“intelligent connectivity”, “deep connectivity”, “holographicconnectivity” and “ubiquitous connectivity”, and the 6G system maysatisfy the requirements shown in Table 1 below. That is, Table 1 showsthe requirements of the 6G system.

TABLE 1 Per device peak data rate 1 Tbps E2E latency 1 ms Maximumspectral efficiency 100 bps/Hz Mobility support Up to 1000 km/hrSatellite Integration Fully AI Fully Autonomous vehicle Fully XR FullyHaptic Communication Fully

At this time, the 6G system may have key factors such as enhanced mobilebroadband (eMBB), ultra-reliable low latency communications (URLLC),massive machine type communications (mMTC), AI integrated communication,tactile Internet, high throughput, high network capacity, high energyefficiency, low backhaul and access network congestion and enhanced datasecurity.

FIG. 2 is a view showing an example of a communication structureprovidable in a 6G system applicable to the present disclosure.

Referring to FIG. 2 , the 6G system will have 50 times highersimultaneous wireless communication connectivity than a 5G wirelesscommunication system. URLLC, which is the key feature of 5G, will becomemore important technology by providing end-to-end latency less than 1 msin 6G communication. At this time, the 6G system may have much bettervolumetric spectrum efficiency unlike frequently used domain spectrumefficiency. The 6G system may provide advanced battery technology forenergy harvesting and very long battery life and thus mobile devices maynot need to be separately charged in the 6G system. In addition, in 6G,new network characteristics may be as follows.

-   -   Satellites integrated network: To provide a global mobile group,        6G will be integrated with satellite. Integrating terrestrial        waves, satellites and public networks as one wireless        communication system may be very important for 6G.    -   Connected intelligence: Unlike the wireless communication        systems of previous generations, 6G is innovative and wireless        evolution may be updated from “connected things” to “connected        intelligence”. AI may be applied in each step (or each signal        processing procedure which will be described below) of a        communication procedure.    -   Seamless integration of wireless information and energy        transfer: A 6G wireless network may transfer power in order to        charge the batteries of devices such as smartphones and sensors.        Therefore, wireless information and energy transfer (WIET) will        be integrated.    -   Ubiquitous super 3-dimension connectivity: Access to networks        and core network functions of drones and very low earth orbit        satellites will establish super 3D connection in 6G ubiquitous.

In the new network characteristics of 6G, several general requirementsmay be as follows.

-   -   Small cell networks: The idea of a small cell network was        introduced in order to improve received signal quality as a        result of throughput, energy efficiency and spectrum efficiency        improvement in a cellular system. As a result, the small cell        network is an essential feature for 5G and beyond 5G (5 GB)        communication systems. Accordingly, the 6G communication system        also employs the characteristics of the small cell network.    -   Ultra-dense heterogeneous network: Ultra-dense heterogeneous        networks will be another important characteristic of the 6G        communication system. A multi-tier network composed of        heterogeneous networks improves overall QoS and reduce costs.    -   High-capacity backhaul: Backhaul connection is characterized by        a high-capacity backhaul network in order to support        high-capacity traffic. A high-speed optical fiber and free space        optical (FSO) system may be a possible solution for this        problem.    -   Radar technology integrated with mobile technology:        High-precision localization (or location-based service) through        communication is one of the functions of the 6G wireless        communication system. Accordingly, the radar system will be        integrated with the 6G network.    -   Softwarization and virtualization: Softwarization and        virtualization are two important functions which are the bases        of a design process in a 5 GB network in order to ensure        flexibility, reconfigurability and programmability.

Core Implementation Technology of 6G System

Artificial Intelligence (AI)

Technology which is most important in the 6G system and will be newlyintroduced is AI. AI was not involved in the 4G system. A 5G system willsupport partial or very limited AI. However, the 6G system will supportAI for full automation. Advance in machine learning will create a moreintelligent network for real-time communication in 6G. When AI isintroduced to communication, real-time data transmission may besimplified and improved. AI may determine a method of performingcomplicated target tasks using countless analysis. That is, AI mayincrease efficiency and reduce processing delay.

Time-consuming tasks such as handover, network selection or resourcescheduling may be immediately performed by using AI. AI may play animportant role even in M2M, machine-to-human and human-to-machinecommunication. In addition, AI may be rapid communication in a braincomputer interface (BCI). An AI based communication system may besupported by meta materials, intelligent structures, intelligentnetworks, intelligent devices, intelligent recognition radios,self-maintaining wireless networks and machine learning.

Recently, attempts have been made to integrate AI with a wirelesscommunication system in the application layer or the network layer, butdeep learning have been focused on the wireless resource management andallocation field. However, such studies are gradually developed to theMAC layer and the physical layer, and, particularly, attempts to combinedeep learning in the physical layer with wireless transmission areemerging. AI-based physical layer transmission means applying a signalprocessing and communication mechanism based on an AI driver rather thana traditional communication framework in a fundamental signal processingand communication mechanism. For example, channel coding and decodingbased on deep learning, signal estimation and detection based on deeplearning, multiple input multiple output (MIMO) mechanisms based on deeplearning, resource scheduling and allocation based on AI, etc. may beincluded.

Machine learning may be used for channel estimation and channel trackingand may be used for power allocation, interference cancellation, etc. inthe physical layer of DL. In addition, machine learning may be used forantenna selection, power control, symbol detection, etc. in the MIMOsystem.

However, application of a deep neutral network (DNN) for transmission inthe physical layer may have the following problems.

Deep learning-based AI algorithms require a lot of training data inorder to optimize training parameters. However, due to limitations inacquiring data in a specific channel environment as training data, a lotof training data is used offline. Static training for training data in aspecific channel environment may cause a contradiction between thediversity and dynamic characteristics of a radio channel.

In addition, currently, deep learning mainly targets real signals.However, the signals of the physical layer of wireless communication arecomplex signals. For matching of the characteristics of a wirelesscommunication signal, studies on a neural network for detecting acomplex domain signal are further required.

Hereinafter, machine learning will be described in greater detail.

Machine learning refers to a series of operations to train a machine inorder to create a machine which can perform tasks which cannot beperformed or are difficult to be performed by people. Machine learningrequires data and learning models. In machine learning, data learningmethods may be roughly divided into three methods, that is, supervisedlearning, unsupervised learning and reinforcement learning.

Neural network learning is to minimize output error. Neural networklearning refers to a process of repeatedly inputting training data to aneural network, calculating the error of the output and target of theneural network for the training data, backpropagating the error of theneural network from the output layer of the neural network to an inputlayer in order to reduce the error and updating the weight of each nodeof the neural network.

Supervised learning may use training data labeled with a correct answerand the unsupervised learning may use training data which is not labeledwith a correct answer. That is, for example, in case of supervisedlearning for data classification, training data may be labeled with acategory. The labeled training data may be input to the neural network,and the output (category) of the neural network may be compared with thelabel of the training data, thereby calculating the error. Thecalculated error is backpropagated from the neural network backward(that is, from the output layer to the input layer), and the connectionweight of each node of each layer of the neural network may be updatedaccording to backpropagation. Change in updated connection weight ofeach node may be determined according to the learning rate. Calculationof the neural network for input data and backpropagation of the errormay configure a learning cycle (epoch). The learning data is differentlyapplicable according to the number of repetitions of the learning cycleof the neural network. For example, in the early phase of learning ofthe neural network, a high learning rate may be used to increaseefficiency such that the neural network rapidly ensures a certain levelof performance and, in the late phase of learning, a low learning ratemay be used to increase accuracy.

The learning method may vary according to the feature of data. Forexample, for the purpose of accurately predicting data transmitted froma transmitter in a receiver in a communication system, learning may beperformed using supervised learning rather than unsupervised learning orreinforcement learning.

The learning model corresponds to the human brain and may be regarded asthe most basic linear model. However, a paradigm of machine learningusing a neural network structure having high complexity, such asartificial neural networks, as a learning model is referred to as deeplearning.

Neural network cores used as a learning method may roughly include adeep neural network (DNN) method, a convolutional deep neural network(CNN) method and a recurrent Boltzmman machine (RNN) method. Such alearning model is applicable.

An artificial neural network is an example of connecting severalperceptrons.

FIG. 3 illustrates a structure of a perceptron to which the methodproposed in the present disclosure can be applied.

Referring to FIG. 3 , when an input vector x=(x1, x2, . . . , xd) isinput, each component is multiplied by a weight (W1, W2, . . . , Wd),and all the results are summed. After that, the entire process ofapplying the activation function σ(·) is called a perceptron. The hugeartificial neural network structure may extend the simplified perceptronstructure shown in FIG. 3 to apply input vectors to differentmultidimensional perceptrons. For convenience of explanation, an inputvalue or an output value is referred to as a node.

Meanwhile, the perceptron structure illustrated in FIG. 3 may bedescribed as being composed of a total of three layers based on an inputvalue and an output value. An artificial neural network in which H (d+1)dimensional perceptrons exist between the 1st layer and the 2nd layer,and K (H+1) dimensional perceptrons exist between the 2nd layer and the3rd layer, as shown in FIG. 4 .

FIG. 4 illustrates the structure of a multilayer perceptron to which themethod proposed in the present disclosure can be applied.

The layer where the input vector is located is called an input layer,the layer where the final output value is located is called the outputlayer, and all layers located between the input layer and the outputlayer are called a hidden layer. In the example of FIG. 4 , three layersare disclosed, but since the number of layers of the artificial neuralnetwork is counted excluding the input layer, it can be viewed as atotal of two layers. The artificial neural network is constructed byconnecting the perceptrons of the basic blocks in two dimensions.

The above-described input layer, hidden layer, and output layer can bejointly applied in various artificial neural network structures such asCNN and RNN to be described later as well as multilayer perceptrons. Thegreater the number of hidden layers, the deeper the artificial neuralnetwork is, and the machine learning paradigm that uses the deep enoughartificial neural network as a learning model is called Deep Learning.In addition, the artificial neural network used for deep learning iscalled a deep neural network (DNN).

FIG. 5 illustrates a structure of a deep neural network to which themethod proposed in the present disclosure can be applied.

The deep neural network shown in FIG. 5 is a multilayer perceptroncomposed of eight hidden layers+output layers. The multilayer perceptronstructure is expressed as a fully-connected neural network. In a fullyconnected neural network, a connection relationship does not existbetween nodes located on the same layer, and a connection relationshipexists only between nodes located on adjacent layers. DNN has a fullyconnected neural network structure and is composed of a combination ofmultiple hidden layers and activation functions, so it can be usefullyapplied to understand the correlation characteristics between input andoutput. Here, the correlation characteristic may mean a jointprobability of input/output.

‘On the other hand, depending on how the plurality of perceptrons areconnected to each other, various artificial neural network structuresdifferent from the aforementioned DNN can be formed.

In a DNN, nodes located inside one layer are arranged in aone-dimensional vertical direction. However, in FIG. 6 , it may beassumed that w nodes are arranged in two dimensions, and h nodes arearranged in a two-dimensional manner (convolutional neural networkstructure of FIG. 6 ). In this case, since a weight is added perconnection in the connection process from one input node to the hiddenlayer, a total of h×w weights must be considered. Since there are h×wnodes in the input layer, a total of h2w2 weights are required betweentwo adjacent layers.

FIG. 6 illustrates the structure of a convolutional neural network towhich the method proposed in the present disclosure can be applied.

The convolutional neural network of FIG. 6 has a problem in that thenumber of weights increases exponentially according to the number ofconnections, so instead of considering the connection of all modesbetween adjacent layers, it is assumed that a filter having a small sizeexists. Thus, as shown in FIG. 7 , weighted sum and activation functioncalculations are performed on a portion where the filters overlap.

One filter has a weight corresponding to the number as much as the size,and learning of the weight may be performed so that a certain feature onan image can be extracted and output as a factor. In FIG. 7 , a filterhaving a size of 3×3 is applied to the upper leftmost 3×3 area of theinput layer, and an output value obtained by performing a weighted sumand activation function operation for a corresponding node is stored inz22.

While scanning the input layer, the filter performs weighted summationand activation function calculation while moving horizontally andvertically by a predetermined interval, and places the output value atthe position of the current filter. This method of operation is similarto the convolution operation on images in the field of computer vision,so a deep neural network with this structure is called a convolutionalneural network (CNN), and a hidden layer generated as a result of theconvolution operation. Is referred to as a convolutional layer. Inaddition, a neural network in which a plurality of convolutional layersexists is referred to as a deep convolutional neural network (DCNN).

FIG. 7 illustrates a filter operation in a convolutional neural networkto which the method proposed in the present disclosure can be applied.

In the convolutional layer, the number of weights may be reduced bycalculating a weighted sum by including only nodes located in a regioncovered by the filter in the node where the current filter is located.Due to this, one filter can be used to focus on features for the localarea. Accordingly, the CNN can be effectively applied to image dataprocessing in which the physical distance in the 2D area is an importantcriterion. Meanwhile, in the CNN, a plurality of filters may be appliedimmediately before the convolution layer, and a plurality of outputresults may be generated through a convolution operation of each filter.

Meanwhile, there may be data whose sequence characteristics areimportant according to data properties. Considering the lengthvariability of the sequence data and the relationship between thesequence data, one element in the data sequence is input at eachtimestep, and the output vector (hidden vector) of the hidden layeroutput at a specific time point is input together with the next elementin the sequence. The structure applied to the artificial neural networkis called a recurrent neural network structure.

FIG. 8 illustrates a neural network structure in which a circular loopto which the method proposed in the present disclosure can be applied.

Referring to FIG. 8 , a recurrent neural network (RNN) is a fullyconnected neural network with elements (x1 (t), x2(t), . . . , xd(t)) ofany line of sight t on a data sequence. In the process of inputting, thepoint t−1 immediately preceding is the weighted sum and activationfunction by inputting the hidden vectors (z1(t−1), z2(t−1), . . . ,zH(t−1)) together. It is a structure to be applied. The reason fortransferring the hidden vector to the next view in this way is thatinformation in the input vector at the previous views is regarded asaccumulated in the hidden vector of the current view.

FIG. 9 illustrates an operation structure of a recurrent neural networkto which the method proposed in the present disclosure can be applied.

Referring to FIG. 9 , the recurrent neural network operates in apredetermined order of time with respect to an input data sequence.

Hidden vectors (z1(1), z2(1), . . . , zH(1)) is input with the inputvector (x1(2), x2(2), . . . , xd(2)) of the time point 2, and the vector(z1(2), z2(2), . . . , zH(2)) is determined. This process is repeatedlyperformed up to the time point 2, time point 3, . . . , time point T.

Meanwhile, when a plurality of hidden layers are disposed in a recurrentneural network, this is referred to as a deep recurrent neural network(DRNN). The recurrent neural network is designed to be usefully appliedto sequence data (for example, natural language processing).

As a neural network core used as a learning method, in addition to DNN,CNN, and RNN, Restricted Boltzmann Machine (RBM), deep belief networks(DBN), and deep Q-networks Network), and can be applied to fields suchas computer vision, speech recognition, natural language processing, andvoice/signal processing.

In recent years, attempts to integrate AI with a wireless communicationsystem have appeared, but this has been concentrated in the field ofwireless resource management and allocation in the application layer,network layer, in particular, deep learning. However, such research isgradually developing into the MAC layer and the physical layer, and inparticular, attempts to combine deep learning with wireless transmissionin the physical layer have appeared. The AI-based physical layertransmission refers to applying a signal processing and communicationmechanism based on an AI driver rather than a traditional communicationframework in the fundamental signal processing and communicationmechanism. For example, deep learning-based channel coding and decoding,deep learning-based signal estimation and detection, deep learning-basedMIMO mechanism, AI-based resource scheduling, and It may includeallocation and the like.

Terahertz (THz) Communication

THz communication is applicable to the 6G system. For example, a datarate may increase by increasing bandwidth. This may be performed byusing sub-TH communication with wide bandwidth and applying advancedmassive MIMO technology. THz waves which are known as sub-millimeterradiation, generally indicates a frequency band between 0.1 THz and 10THz with a corresponding wavelength in a range of 0.03 mm to 3 mm. Aband range of 100 GHz to 300 GHz (sub THz band) is regarded as a mainpart of the THz band for cellular communication. When the sub-THz bandis added to the mmWave band, the 6G cellular communication capacityincreases. 300 GHz to 3 THz of the defined THz band is in a far infrared(IR) frequency band. A band of 300 GHz to 3 THz is a part of an opticalband but is at the border of the optical band and is just behind an RFband. Accordingly, the band of 300 GHz to 3 THz has similarity with RF.

The main characteristics of THz communication include (i) bandwidthwidely available to support a very high data rate and (ii) high pathloss occurring at a high frequency (a high directional antenna isindispensable). A narrow beam width generated in the high directionalantenna reduces interference. The small wavelength of a THz signalallows a larger number of antenna elements to be integrated with adevice and BS operating in this band. Therefore, an advanced adaptivearrangement technology capable of overcoming a range limitation may beused.

Optical Wireless Technology

Optical wireless communication (OWC) technology is planned for 6Gcommunication in addition to RF based communication for all possibledevice-to-access networks. This network is connected to anetwork-to-backhaul/fronthaul network connection. OWC technology hasalready been used since 4G communication systems but will be more widelyused to satisfy the requirements of the 6G communication system. OWCtechnologies such as light fidelity/visible light communication, opticalcamera communication and free space optical (FSO) communication based onwide band are well-known technologies. Communication based on opticalwireless technology may provide a very high data rate, low latency andsafe communication. Light detection and ranging (LiDAR) may also be usedfor ultra high resolution 3D mapping in 6G communication based on wideband.

FSO Backhaul Network

The characteristics of the transmitter and receiver of the FSO systemare similar to those of an optical fiber network. Accordingly, datatransmission of the FSO system similar to that of the optical fibersystem. Accordingly, FSO may be a good technology for providing backhaulconnection in the 6G system along with the optical fiber network. WhenFSO is used, very long-distance communication is possible even at adistance of 10,000 km or more. FSO supports mass backhaul connectionsfor remote and non-remote areas such as sea, space, underwater andisolated islands. FSO also supports cellular base station connections.

Massive MIMO Technology

One of core technologies for improving spectrum efficiency is MIMOtechnology. When MIMO technology is improved, spectrum efficiency isalso improved. Accordingly, massive MIMO technology will be important inthe 6G system. Since MIMO technology uses multiple paths, multiplexingtechnology and beam generation and management technology suitable forthe THz band should be significantly considered such that data signalsare transmitted through one or more paths.

Blockchain

A blockchain will be important technology for managing large amounts ofdata in future communication systems. The blockchain is a form ofdistributed ledger technology, and distributed ledger is a databasedistributed across numerous nodes or computing devices. Each nodeduplicates and stores the same copy of the ledger. The blockchain ismanaged through a peer-to-peer (P2P) network. This may exist withoutbeing managed by a centralized institution or server. Blockchain data iscollected together and organized into blocks. The blocks are connectedto each other and protected using encryption. The blockchain completelycomplements large-scale IoT through improved interoperability, security,privacy, stability and scalability. Accordingly, the blockchaintechnology provides several functions such as interoperability betweendevices, high-capacity data traceability, autonomous interaction ofdifferent IoT systems, and large-scale connection stability of 6Gcommunication systems.

3D Networking

The 6G system integrates terrestrial and public networks to supportvertical expansion of user communication. A 3D BS will be providedthrough low-orbit satellites and UAVs. Adding new dimensions in terms ofaltitude and related degrees of freedom makes 3D connectionssignificantly different from existing 2D networks.

Quantum Communication

In the context of the 6G network, unsupervised reinforcement learning ofthe network is promising. The supervised learning method cannot labelthe vast amount of data generated in 6G. Labeling is not required forunsupervised learning. Thus, this technique can be used to autonomouslybuild a representation of a complex network. Combining reinforcementlearning with unsupervised learning may enable the network to operate ina truly autonomous way.

Unmanned Aerial Vehicle

An unmanned aerial vehicle (UAV) or drone will be an important factor in6G wireless communication. In most cases, a high-speed data wirelessconnection is provided using UAV technology. A base station entity isinstalled in the UAV to provide cellular connectivity. UAVs have certainfeatures, which are not found in fixed base station infrastructures,such as easy deployment, strong line-of-sight links, andmobility-controlled degrees of freedom. During emergencies such asnatural disasters, the deployment of terrestrial telecommunicationsinfrastructure is not economically feasible and sometimes servicescannot be provided in volatile environments. The UAV can easily handlethis situation. The UAV will be a new paradigm in the field of wirelesscommunications. This technology facilitates the three basic requirementsof wireless networks, such as eMBB, URLLC and mMTC. The UAV can alsoserve a number of purposes, such as network connectivity improvement,fire detection, disaster emergency services, security and surveillance,pollution monitoring, parking monitoring, and accident monitoring.Therefore, UAV technology is recognized as one of the most importanttechnologies for 6G communication.

Cell-free Communication

The tight integration of multiple frequencies and heterogeneouscommunication technologies is very important in the 6G system. As aresult, a user can seamlessly move from network to network withouthaving to make any manual configuration in the device. The best networkis automatically selected from the available communication technologies.This will break the limitations of the cell concept in wirelesscommunication. Currently, user movement from one cell to another cellcauses too many handovers in a high-density network, and causes handoverfailure, handover delay, data loss and ping-pong effects. 6G cell-freecommunication will overcome all of them and provide better QoS.Cell-free communication will be achieved through multi-connectivity andmulti-tier hybrid technologies and different heterogeneous radios in thedevice.

Wireless Information and Energy Transfer (WIET)

WIET uses the same field and wave as a wireless communication system. Inparticular, a sensor and a smartphone will be charged using wirelesspower transfer during communication. WIET is a promising technology forextending the life of battery charging wireless systems. Therefore,devices without batteries will be supported in 6G communication.

Integration of Sensing and Communication

An autonomous wireless network is a function for continuously detectinga dynamically changing environment state and exchanging informationbetween different nodes. In 6G, sensing will be tightly integrated withcommunication to support autonomous systems.

Integration of Access Backhaul Network

In 6G, the density of access networks will be enormous. Each accessnetwork is connected by optical fiber and backhaul connection such asFSO network. To cope with a very large number of access networks, therewill be a tight integration between the access and backhaul networks.

Hologram Beamforming

Beamforming is a signal processing procedure that adjusts an antennaarray to transmit radio signals in a specific direction. This is asubset of smart antennas or advanced antenna systems. Beamformingtechnology has several advantages, such as high signal-to-noise ratio,interference prevention and rejection, and high network efficiency.Hologram beamforming (HBF) is a new beamforming method that differssignificantly from MIMO systems because this uses a software-definedantenna. HBF will be a very effective approach for efficient andflexible transmission and reception of signals in multi-antennacommunication devices in 6G.

Big Data Analysis

Big data analysis is a complex process for analyzing various large datasets or big data. This process finds information such as hidden data,unknown correlations, and customer disposition to ensure complete datamanagement. Big data is collected from various sources such as video,social networks, images and sensors. This technology is widely used forprocessing massive data in the 6G system.

Large Intelligent Surface (LIS)

In the case of the THz band signal, since the straightness is strong,there may be many shaded areas due to obstacles. By installing the LISnear these shaded areas, LIS technology that expands a communicationarea, enhances communication stability, and enables additional optionalservices becomes important. The LIS is an artificial surface made ofelectromagnetic materials, and can change propagation of incoming andoutgoing radio waves. The LIS can be viewed as an extension of massiveMIMO, but differs from the massive MIMO in array structures andoperating mechanisms. In addition, the LIS has an advantage such as lowpower consumption, because this operates as a reconfigurable reflectorwith passive elements, that is, signals are only passively reflectedwithout using active RF chains. In addition, since each of the passivereflectors of the LIS must independently adjust the phase shift of anincident signal, this may be advantageous for wireless communicationchannels. By properly adjusting the phase shift through an LIScontroller, the reflected signal can be collected at a target receiverto boost the received signal power.

Terahertz (THz) Wireless Communications in General

THz wireless communication uses a THz wave having a frequency ofapproximately 0.1 to 10 THz (1 THz=1012 Hz), and may mean terahertz(THz) band wireless communication using a very high carrier frequency of100 GHz or more. The THz wave is located between radio frequency(RF)/millimeter (mm) and infrared bands, and (i) transmitsnon-metallic/non-polarizable materials better than visible/infrared raysand has a shorter wavelength than the RF/millimeter wave and thus highstraightness and is capable of beam convergence. In addition, the photonenergy of the THz wave is only a few meV and thus is harmless to thehuman body. A frequency band which will be used for THz wirelesscommunication may be a D-band (110 GHz to 170 GHz) or a H-band (220 GHzto 325 GHz) band with low propagation loss due to molecular absorptionin air. Standardization discussion on THz wireless communication isbeing discussed mainly in IEEE 802.15 THz working group (WG), inaddition to 3GPP, and standard documents issued by a task group (TG) ofIEEE 802.15 (e.g., TG3d, TG3e) specify and supplement the description ofthis disclosure. The THz wireless communication may be applied towireless cognition, sensing, imaging, wireless communication, and THznavigation.

FIG. 11 is a view showing a THz communication method applicable to thepresent disclosure.

Referring to FIG. 11 , a THz wireless communication scenario may beclassified into a macro network, a micro network, and a nanoscalenetwork. In the macro network, THz wireless communication may be appliedto vehicle-to-vehicle (V2V) connection and backhaul/fronthaulconnection. In the micro network, THz wireless communication may beapplied to near-field communication such as indoor small cells, fixedpoint-to-point or multi-point connection such as wireless connection ina data center or kiosk downloading.

Table 2 below shows an example of technology which may be used in theTHz wave.

TABLE 2 Transceivers Device Available immature: UTC-PD, RTD and SBDModulation and coding Low order modulation techniques (OOK, QPSK), LDPC,Reed Soloman, Hamming, Polar, Turbo Antenna Omni and Directional, phasedarray with low number of antenna elements Bandwidth 69 GHz (or 23 GHz)at 300 GHz Channel models Partially Data rate 100 Gbps Outdoordeployment No Free space loss High Coverage Low Radio Measurements 300GHz indoor Device size Few micrometers

THz wireless communication can be classified based on a method forgenerating and receiving THz. The THz generation method can beclassified as an optical device or an electronic device-basedtechnology.

FIG. 12 is a view showing a THz wireless communication transceiverapplicable to the present disclosure.

The method of generating THz using an electronic device includes amethod using a semiconductor device such as a resonance tunneling diode(RTD), a method using a local oscillator and a multiplier, a monolithicmicrowave integrated circuit (MMIC) method using a compoundsemiconductor high electron mobility transistor (HEMT) based integratedcircuit, and a method using a Si-CMOS-based integrated circuit. In thecase of FIG. 18 , a multiplier (doubler, tripler, multiplier) is appliedto increase the frequency, and radiation is performed by an antennathrough a subharmonic mixer. Since the THz band forms a high frequency,a multiplier is essential. Here, the multiplier is a circuit having anoutput frequency which is N times an input frequency, and matches adesired harmonic frequency, and filters out all other frequencies. Inaddition, beamforming may be implemented by applying an array antenna orthe like to the antenna of FIG. 18 . In FIG. 18 , IF represents anintermediate frequency, a tripler and a multiplier represents amultiplier, PA represents a power amplifier, and LNA represents a lownoise amplifier, and PLL represents a phase-locked loop.

FIG. 13 is a view showing a THz signal generation method applicable tothe present disclosure and FIG. 14 is a view showing a wirelesscommunication transceiver applicable to the present disclosure.

Referring to FIGS. 13 and 14 , the optical device-based THz wirelesscommunication technology means a method of generating and modulating aTHz signal using an optical device. The optical device-based THz signalgeneration technology refers to a technology that generates anultrahigh-speed optical signal using a laser and an optical modulator,and converts it into a THz signal using an ultrahigh-speedphotodetector. This technology is easy to increase the frequencycompared to the technology using only the electronic device, cangenerate a high-power signal, and can obtain a flat responsecharacteristic in a wide frequency band. In order to generate the THzsignal based on the optical device, as shown in FIG. 13 , a laser diode,a broadband optical modulator, and an ultrahigh-speed photodetector arerequired. In the case of FIG. 13 , the light signals of two lasershaving different wavelengths are combined to generate a THz signalcorresponding to a wavelength difference between the lasers. In FIG. 13, an optical coupler refers to a semiconductor device that transmits anelectrical signal using light waves to provide coupling with electricalisolation between circuits or systems, and a uni-travelling carrierphotodetector (UTC-PD) is one of photodetectors, which uses electrons asan active carrier and reduces the travel time of electrons by bandgapgrading. The UTC-PD is capable of photodetection at 150 GHz or more. InFIG. 14 , an erbium-doped fiber amplifier (EDFA) represents an opticalfiber amplifier to which erbium is added, a photo detector (PD)represents a semiconductor device capable of converting an opticalsignal into an electrical signal, and OSA represents an optical subassembly in which various optical communication functions (e.g.,photoelectric conversion, electrophotic conversion, etc.) aremodularized as one component, and DSO represents a digital storageoscilloscope.

The structure of a photoelectric converter (or photoelectric converter)will be described with reference to FIGS. 15 and 16 . FIG. 15 is a viewshowing a transmitter structure based on a photonic source applicable tothe present disclosure. FIG. 16 is a view showing an optical modulatorstructure applicable to the present disclosure.

generally, the optical source of the laser may change the phase of asignal by passing through the optical wave guide. At this time, data iscarried by changing electrical characteristics through microwave contactor the like. Thus, the optical modulator output is formed in the form ofa modulated waveform. A photoelectric modulator (O/E converter) maygenerate THz pulses according to optical rectification operation by anonlinear crystal, photoelectric conversion (O/E conversion) by aphotoconductive antenna, and emission from a bunch of relativisticelectrons. The terahertz pulse (THz pulse) generated in the above mannermay have a length of a unit from femto second to pico second. Thephotoelectric converter (O/E converter) performs down conversion usingnon-linearity of the device.

Given THz spectrum usage, multiple contiguous GHz bands are likely to beused as fixed or mobile service usage for the terahertz system.According to the outdoor scenario criteria, available bandwidth may beclassified based on oxygen attenuation 10{circumflex over ( )}2 dB/km inthe spectrum of up to 1 THz. Accordingly, a framework in which theavailable bandwidth is composed of several band chunks may beconsidered. As an example of the framework, if the length of theterahertz pulse (THz pulse) for one carrier (carrier) is set to 50 ps,the bandwidth (BW) is about 20 GHz.

Effective down conversion from the infrared band to the terahertz banddepends on how to utilize the nonlinearity of the O/E converter. Thatis, for down-conversion into a desired terahertz band (THz band), designof the photoelectric converter (O/E converter) having the most idealnon-linearity to move to the corresponding terahertz band (THz band) isrequired. If a photoelectric converter (O/E converter) which is notsuitable for a target frequency band is used, there is a highpossibility that an error occurs with respect to the amplitude and phaseof the corresponding pulse.

In a single carrier system, a terahertz transmission/reception systemmay be implemented using one photoelectric converter. In a multi-carriersystem, as many photoelectric converters as the number of carriers maybe required, which may vary depending on the channel environment.Particularly, in the case of a multi-carrier system using multiplebroadbands according to the plan related to the above-described spectrumusage, the phenomenon will be prominent. In this regard, a framestructure for the multi-carrier system can be considered. Thedown-frequency-converted signal based on the photoelectric converter maybe transmitted in a specific resource region (e.g., a specific frame).The frequency domain of the specific resource region may include aplurality of chunks. Each chunk may be composed of at least onecomponent carrier (CC).

Phase Tracking Reference Signal (PTRS)

In the case of mmWave, since an influence of phase noise is large due todamaged to RF hardware, a transmitted or received signal is distorted ina time domain.

The phase noise causes common phase error (CPE) and inter-carrierinterference (ICI) in the frequency domain.

In particular, compensation of oscillator phase noise is enabled in ahigh carrier frequency, and the same phase rotation for all subcarriersoccurs by the phase noise. Accordingly, the PTRS is defined in the NR inorder to estimate and compensate the CPE.

Hereinafter, a DL PTRS related operation will be described.

FIG. 17 is a flowchart illustrating an example of a DL PTRS procedure.

Referring to FIG. 17 , the BS transmits, to the UE, PTRS configurationinformation (S1710).

The PTRS configuration information may refer to PTRS-DownlinkConfig IE.

The PTRS-DownlinkConfig IE may include parameter frequencyDensity,parameter timeDensity, parameter epre-Ratio, parameterresourceElementOffset, etc.

The parameter frequencyDensity as a function of scheduled BW is aparameter representing presence and a frequency density of DL PTRS.

The parameter timeDensity as a function of modulation and coding scheme(MCS) is a parameter representing the presence and a time density of DLPTRS.

The parameter epre-Ratio is a parameter representing energy per resourceelement between the PTRS and the PDSCH.

Next, the BS generates a sequence used for the PTRS (S1720).

The sequence for the PTRS is generated a DMRS sequence of the samesubcarrier as shown in Equation 4.1C-3 below.

Sequence generation for the PTRS may be defined differently according towhether the transform precoding is enabled and Equation 4.1C-3 shows anexample when the transform precoding is disabled.

r _(k) =r(2m+k′)  [Equation 1]

Here, r(2m+k′) is a DMRS given at location l₀ and subcarrier k.

That is, the sequence of the PTRS uses the sequence of the DMRS, butmore specifically, the sequence of the PTRS in subcarrier k is the sameas the sequence of the DMRS in subcarrier k.

Next, the BS maps the generated sequence to a resource element (S1730).

Here, the resource element may include at least one of a time, afrequency, an antenna port, or a code.

The position in the time domain of the PTRS is mapped at a specificsymbol interval starting from a start symbol of PDSCH allocation, butwhen there is the DMRS symbol, mapping is performed from a next symbolof the corresponding DMRS symbol. The specific symbol interval may be 1,2, or 4 symbols.

In addition, in relation to resource element mapping of the PTRS, afrequency position of the PTRS is determined by a frequency position ofan associated DMRS port and higher layer parameter UL-PTRS-RE-offset.

Here, the UL-PTRS-RE-offset is included in a PTRS configuration, andindicates a subcarrier offset for UL PTRS for CP-OFDM.

For DL, a PTRS port is associated with a DMRS port having a lowest indexbetween scheduled DMRS ports.

In addition, for UL, the BS configures which DMRS port is associatedwith the PTRS port through the UL DCI

Next, the BS transmits, to the UE, the PTRS on the resource element(S1740). The UE performs compensation for the phase noise by using thereceived PTRS.

Hereinafter, a UL PTRS related operation will be described.

A UL PTRS related operation is similar to the UL PTRS related operationdescribed above, and names of the parameters related to DL may bereplaced with the names of parameters related to UL.

That is, PTRS-DownlinkConfig IE may be replaced with PTRS-UplinkConfigIE, and the DL PTRS related operation, the BS may be replaced with theUE and the UE may be replaced with the BS.

Similarly, sequence generation for the PTRS may be defined differentlyaccording to whether transform decoding is enabled.

The aforementioned contents may be combined with subsequent embodimentsproposed in the present disclosure and applied or may be supplemented toclarify technical characteristics of the embodiments proposed in thepresent disclosure. Hereinafter, the embodiments to be describedhereinafter have been divided for convenience of description only, andsome elements of any one embodiment may be substituted with someelements of another embodiment or may be mutually combined and applied.

Compared to the existing LTE mobile communication, 5G mobilecommunication increases the influence of phase noise (PN) as the centerfrequency rises to the mmWave band. Since an increase in phase noiseresults in a decrease in the achievable Signal Noise to Ratio (SINR) ofthe signal, the maximum capacity of the entire system is limited. InLTE, the phase noise is large enough not to affect communication, but asthe center frequency increases to the mmWave band, the phase noiseincreases to a size large enough to affect communication, andcompensation is required.

In this regard, in 5G NR, a method for removing a Common Phase Error(CPE) of a PN using a pilot signal called a Phase Tracking ReferenceSignal (PTRS) is utilized.

The present disclosure proposes a method for controlling the phase noise(PN) more effectively than a conventional method for removing the phasenoise based on the PTRS. First, the problems of the conventional schemeare described.

The CPE estimation based on the existing PTRS is performed as follows.Specifically, a mean value of PNs affecting an OFDM symbol is measuredusing the pilot of the frequency domain. Hereinafter, the estimation ofthe phase noise will be described with reference to FIG. 18 .

FIG. 18 is a graph for describing estimation of a phase noise using acommon phase error (CPE) according to the prior art. Referring to FIG.18 , the CPE represents the mean of PN integral values. The phase noiseestimation method using the CPE shows satisfactory performance when achange in phase noise (PN) in one OFDM symbol is small.

However, when a change in phase noise in one OFDM symbol increases dueto an increase in frequency, the performance of the phase noiseestimation method is limited (i.e., the accuracy of phase noiseestimation decreases).

The difference between the actual PN value ({circle around (1)}) and thecompensated CPE value ({circle around (2)}) acts as InterSymbolInterference (ISI), reducing the achievable SINR of the signal. In ahigh PN Power Spectrum Density (PSD) situation, since the difference(i.e., the difference between {circle around (1)} and {circle around(2)}) becomes larger, the phase noise estimation performance is greatlydeteriorated.

To overcome this problem, an interpolation method is proposed. The PNmay be estimated in a form similar to an actual PN value through linearinterpolation at the center of a symbol based on a CPE value ({circlearound (3)}).

This method has two problems. First, there is a difference between theCPE value and the actual PN value at the central point of the symbol asa reference. Second, there is no reference for interpolation. As such,since the characteristics of an actual PN are not considered at all,performance enhancement of phase noise estimation through interpolationis not large.

As reviewed above, an object of the present disclosure is to improve theperformance of an interpolation-based PN compensation scheme using thePTRS.

Hereinafter, two methods are proposed to maximize the performance ofinterpolation-based PN compensation using the existing PTRS.

First, since the length of an OFDM symbol is long, it is difficult touse the CPE, which is the man value of the phase noise of symbols, as avalue representing a PN value at a specific time. That is, the CPE valuewhich becomes the existing interpolation reference does not indicate thevalue of the PN at the center of the actual symbol. To solve thisproblem, a pilot transmitted in a very short time region compared to theOFDM symbol is used. An accurate PN value may be measured at a specifictime based on the pilot. The interpolation may be performed based on themeasured PN value.

After performing the second conventional interpolation method, the meanvalue of the integral of PN, which is a basic property of the estimatedPN, is not equal to the value of CPE, resulting in an error inestimating the phase noise. In order to make the estimated PN valueafter interpolation the same as the CPE value of the existing PN, a newinterpolation method is presented.

Through the above two methods, higher performance may be obtained thanthe interpolation method using the existing CPE. In the presentdisclosure, the CPE of the PN is estimated in the frequency domain andthe time domain in one symbol, and in order to estimate the CPE in thefrequency domain, a PTRS-based pilot configuration is used, and in orderto estimate the CPE in the time domain, a known pilot is used.

FIG. 19 is a view for explaining a pilot structure according to anembodiment of the present disclosure by comparing with a conventionalscheme. (a) of FIG. 19 and (c) of FIG. 19 illustrate a pilot structurebased on the prior art, and (b) of FIG. 19 and (d) of FIG. 19 illustratea pilot structure based on an embodiment of the present disclosure.

Referring to (a) of FIG. 19 , in the existing PTRS-based pilotstructure, a PTRS pilot is inserted at a specific location in thefrequency domain. The conventional method estimates the CPE of the OFDMsymbol using the PTRS pilot.

Referring to (c) of FIG. 19 , the method according to the embodiment ofthe present disclosure inserts a part of a PTRS pilot into a PN pilot inthe time domain. That is, the method according to the embodiment of thepresent disclosure utilizes two pilots. Specifically, the PTRS pilot isused for calculating the CPE in the frequency domain, and the PN pilotis used for calculating the PN in the time domain.

Hereinafter, for convenience of description, a PN value calculated basedon the PN pilot may be referred to as first phase noise representing aPN value at a specific time. The term is used to differentiate from thefinally estimated phase noise, and is not used to limit the technicalscope.

FIG. 20 illustrates a structure of a pilot according to an embodiment ofthe present disclosure.

(a) of FIG. 20 illustrates a pilot structure according to a conventionalmethod.

(b) of FIG. 20 illustrates a pilot structure according to method 1 in anembodiment of the present disclosure. According to method 1, instead ofremoving P PTRS pilots in the frequency domain, P PN pilots are insertedin the time domain. Here, if the PN pilot is placed after a CyclicPrefix (CP), inter-symbol interference (ISI) may be caused to thecorresponding symbol, so the PN pilot is placed before the CP. In thiscase, the overall symbol length is increased, but the total throughputmay be maintained.

Method 1 has a disadvantage in that system change (i.e., frame structurechange) is required because the length of the OFDM symbol is changed. Inorder to overcome the disadvantage of Method 1, Method 2 according to anembodiment of the present disclosure may be considered.

Referring to (c) of FIG. 20 , in method 2, a part of the CP is replacedwith the PN pilot. More specifically, the frontmost part of the CP maybe replaced with the PN pilot. In the case of CP, since the length ofthe CP it is set larger than the maximum channel length, there is verylittle possibility of inter-symbol interference even when a shorter PNpilot is inserted than the CP length. In addition, even if theinter-symbol interference occurs, if the channel and PN values may beknown, the interference may be removed through additional basebandprocessing.

In the case of the method 2, although a slight deterioration of signalquality or additional complexity according to signal processing may becaused, there is an advantage of maintaining the conventional framestructure.

Hereinafter, a method for estimating phase noise (PN) according to anembodiment of the present disclosure will be described.

FIG. 21 is a block diagram for describing a method for estimating aphase noise according to an embodiment of the present disclosure.

Referring to FIG. 21 , a PN value may be calculated using a PN pilot(known pilot) in the time domain, and a mean value obtained byintegrating the corresponding value may be estimated as the CPE(Estimate PN pilot CPE). In the frequency domain, the CPE may beestimated based on the PTRS pilot (Estimate PTRS CPE). The PN isestimated based on interpolation using the CPE values.

Hereinafter, a method first estimating the first phase noise based onthe PN pilot will be described in detail.

Estimation of the first phase noise in the time domain may be performedbased on Equations 2 and 3 below.

y _(pilot)(t)=e ^(|PN(t)) P _(pilot)(t)  [Equation 2]

In Equation 2, y_(pilot)(t) represents a signal received by the wirelessdevice (e.g., UE/BS). The received signal is expressed in the form of amultiplication of a transmitted PN pilot signal P_(pilot)(t) and PN(t)which is the PN in the time domain.

PN_(est)(q) which is a PN value (i.e., a first phase noise) estimated inthe time domain in a qth symbol may be expressed as in Equation 3 below.

$\begin{matrix}{{{PN}_{est}(q)} = {{\frac{1}{P}{\sum\limits_{t = 0}^{N_{time} - 1}{\angle\frac{y_{pilot}(t)}{P_{pilot}(t)}}}} = {\frac{1}{N_{time}}{\sum\limits_{N = 0}^{N_{time} - 1}\left\lbrack {{PN}(t)} \right\rbrack}}}} & \left\lbrack {{Equation}3} \right\rbrack\end{matrix}$

In Equation 3 above, N_(time) represents the length of the PN pilot andP represents the number of PN pilots. Referring to Equation 3 above,PN_(est)(q) may be estimated by a mean value of PN(t) of the timedomain. This is a method similar to the estimation of the CPE calculatedin the frequency domain by using the PTRS pilot, but the CPE isestimated in a short pilot time, so an actual PN value of a time domainpilot interval may be represented.

The CPE estimation in the frequency domain utilizing the PTRS pilot inthe qth symbol may be performed based on Equation 4 below.

$\begin{matrix}{{{{CPE}_{PTRS}(q)} = {\angle\frac{1}{N_{PTRS}}{\sum\limits_{n = 0}^{N_{PTRS} - 1}\frac{Y\left\lbrack {p(n)} \right\rbrack}{{H\left\lbrack {p(n)} \right\rbrack}{P_{pilot}\lbrack n\rbrack}}}}},} & \left\lbrack {{Equation}4} \right\rbrack\end{matrix}$ p(n) = PTRSpilotsubcarrierindex

Here, Y[p(n)] represents a received signal according to H[p(n)]represents a channel according to p(n), N_(PTRS) represents the numberof PTRS pilots, p(n) represents a subcarrier index of an nth PTRS pilot,P_(pilot)[n] and represents an nth PTRS pilot.

Hereinafter, matters related to selection of an interpolation referencefor phase noise estimation will be described.

FIG. 22 is a graph for describing a reference point of interpolationperformed for estimation of a phase noise according to an embodiment ofthe present disclosure.

Referring to FIG. 22 , the mean of integral values of the PN is equal tothe CPE value. In the case of using the conventional interpolationmethod, the integral value (mean of) of the estimated PN values is notequal to the CPE value. To solve this problem, the phase estimationmethod according to an embodiment of the present disclosure proposes theuse of a new reference point.

According to an embodiment, the new reference point may be based on thefirst phase noise calculated in the time domain and the CPE calculatedin the frequency domain. Specifically, the new reference point may bebased on the first phase noise and the CPE.

In this case, the first phase noise may include values calculated forthe current symbol and the next symbol. Specifically, the first phasenoise may include a PN_(est)(q) value of a current symbol (symbol indexq) and a PN_(est)(q+1) value of a next symbol (symbol index q+1).

In FIG. 22 , the new reference point is PN_(m), and, a straight line ofPN_(est)(q) and PN_(m) is expressed as PN1(t), and a straight line ofPN_(m) and PN_(est)(q+1) is expressed as PN2(t). As a result,CPE_(PTRS)(q) which is the CPE value of the current symbol (symbol indexq) may be expressed as in Equation 5 below.

$\begin{matrix}{{\frac{1}{N}\left( {{\int_{0}^{\frac{N}{2}}{{PN}1(t){dt}}} + {\int_{\frac{N}{2}}^{N}{{PN}2(t){dt}}}} \right)} = {{CPE}_{PTRS}(q)}} & \left\lbrack {{Equation}5} \right\rbrack\end{matrix}$

PN_(m)(q) which is the new reference point of the interpolation may beexpressed as in Equation 5 above and Equation 6 below by usingPN_(est)(q) and PN_(est)(q+1).

PN _(m)(q)=2CPE _(PTRS)(q)−½PN _(est)(q)−½PN _(est)(q+1)  [Equation 6]

The phase noise PN in the time domain estimated through theinterpolation based on the PN_(m)(q) may be expressed as in Equation 7below.

$\begin{matrix}{{\overset{\sim}{PN}(t)} = \text{ }\left\lbrack \begin{matrix}{{{{PN}1(t)} = {{\frac{2}{N}\left( {{{PN}_{m}(q)} - {{PN}_{est}(q)}} \right)t} + {{PN}_{est}(q)}}},\text{ }{t = {{\left( {q - 1} \right){\left. N \right.\sim\left( {q - 1} \right)}N} + \frac{N}{2}}}} \\{{{{PN}2(t)} = {{\frac{2}{N}\left( {{{PN}_{est}\left( {q + 1} \right)} - {{PN}_{m}(q)}} \right)\left( {t - \frac{N}{2}} \right)} + {{PN}_{m}(q)}}},\text{ }{t = {{\left( {q - 1} \right)N} + {\left. \frac{N}{2} \right.\sim{qN}}}}}\end{matrix} \right.} & \left\lbrack {{Equation}7} \right\rbrack\end{matrix}$

In Equation 7 above, q represents the index of the OFDM symbol, and Nrepresents one OFDM symbol length.

Hereinafter, in FIGS. 23 to 25 , performance of phase noise estimationaccording to an embodiment of the present disclosure will be describedin comparison with the conventional method.

FIG. 23 is a diagram for describing the performance of phase noiseestimation according to the method proposed in the present disclosure bycomparing with the prior art.

FIG. 23 is a graph illustrating four phase noises.

Specifically, the graph shows 1) actual phase noise (PN original), 2) PNestimated from the CPE calculated based on the conventional PTRS (PNPTRS), 3) PN estimated using interpolation with the CPE (PTRSinterpolation), and 4) PN estimated according to the method proposed inthe present disclosure (Proposed scheme).

Referring to FIG. 23 , it may be identified that the proposed scheme ofthe present disclosure may more accurately estimate the PN in the timedomain compared to the conventional scheme according to 2) and 3). Theform of the PN estimated according to the proposed scheme of the presentdisclosure is similar to the form of the actual PN (PN original).

FIGS. 24 and 25 are diagrams for describing a block error rate (BER)when the method proposed in the present disclosure is applied bycomparing with the conventional scheme.

FIGS. 24 and 25 illustrate block error rates (BER) in an additive whiteGaussian noise channel.

FIG. 24 is a graph illustrating a block error rate (BER) according toSignal to Noise Ratio (SNR) when PN Power Spectrum Density (PSD) is −67dBc/Hz and 64 Quadrature Amplitude Modulation (QAM) is used as amodulation scheme.

FIG. 25 is a graph illustrating a block error rate (BER) according tothe PN power spectrum density (PSD) when the signal to noise ratio (SNR)is 20 dB and 64 Quadrature Amplitude Modulation (QAM) is used as themodulation scheme.

Referring to FIGS. 24 and 25 , it may be identified that the methodaccording to the embodiment of the present disclosure (proposed scheme)has a BER gain of up to 40% compared to the conventional scheme (PTRSinterpolation).

Hereinafter, effects of the method for estimating the phase noiseaccording to an embodiment of the present disclosure will be described.

The method for estimating the phase noise according to theabove-described embodiment may be used to improve the performance of a5G NR scheme (a method for compensating the PN based on the PTRS).

The method for estimating the phase noise according to an embodiment ofthe present disclosure utilizes a conventional CP interval as aninterval through which the PN pilot is transmitted, so the effect on theentire system may be minimized. That is, the method for estimating thephase noise according to the embodiment has backward compatibility.

When communication is performed in a high frequency band, the powerspectral density (PSD) of phase noise (PN) increases significantly, andthus PN estimation and compensation act as very important factors. Themethod for estimating the phase noise according to an embodiment of thepresent disclosure may more effectively support a wireless communicationsystem (Wifi using 60 GHz, 6G communication system) utilizing a highfrequency band.

That is, according to an embodiment of the present disclosure, the phasenoise may be estimated more accurately than conventional scheme.Accordingly, 1) performance in terms of the block error rate (BER) maybe enhanced and 2) total throughput may be increased because a higherModulation and Coding Scheme level (MCS) may be used.

As described above, according to an embodiment of the presentdisclosure, performance of high frequency band communication may beimproved and reliability may be guaranteed.

In terms of implementation, the operations related to the estimation ofthe phase noise according to the above-described embodiments may beprocessed by apparatuses (e.g., processors 102 and 202 in FIG. 28 ) inFIGS. 27 to 31 to be described below.

Further, the operations related the estimation of the phase noiseaccording to the above-described embodiment may also be stored inmemories (e.g., 104 and 204 in FIG. 28 ) in the form of aninstruction/program (e.g., instruction or executable code) for drivingat least one processor (e.g., 102 and 202 in FIG. 28 ).

Hereinafter, the above-described embodiments will be described in detailwith reference to FIG. 26 in terms of the operation of the wirelessdevice. Methods to be described below are just distinguished forconvenience and it is needless to say that some components of any onemethod may be substituted with some components of another method or maybe applied in combination with each other. The wireless device may be awireless device (e.g., a UE/a BS) based on FIGS. 27 to 31 to bedescribed later.

FIG. 26 is a flowchart for describing a method for estimating, by awireless device, a phase noise in a wireless communication systemaccording to an embodiment of the present disclosure.

Referring to FIG. 26 , a method for estimating phase noise by a wirelessdevice in a wireless communication system according to an embodiment ofthe present disclosure includes: receiving a pilot signal and a phasetracking reference signal (S2610); calculating a first phase noise basedon the pilot signal (S2620); calculating a common phase error based onthe phase tracking reference signal (S2630); and estimating phase noisebased on the first phase noise and the common phase error (S2640).

In S2610, the wireless device (UE or BS) receives, from another wirelessdevice (BS or UE), a pre-configured pilot signal and a phase trackingreference signal (PTRS). The pre-configured pilot signal may be a signalbased on a PN pilot according to the above-described embodiment. In thiscase, the pre-configured pilot signal may be configured by PTRSconfiguration information of FIG. 17 .

The pre-configured pilot signal and the phase tracking reference signalmay be transmitted through an uplink channel or a downlink channel. Forexample, when the phase tracking reference signal is transmitted throughthe downlink channel, the wireless device may be the UE and the otherwireless device may be the BS. As another example, when the phasetracking reference signal is transmitted through the uplink channel, thewireless device may be the BS and the other wireless device may be theUE.

According to an embodiment, the pre-configured pilot signal may betransmitted in one region of a time domain allocated for a cyclic prefix(CP) of the PTRS. The one region may be positioned at a frontmostportion of the time domain allocated for the CP. The embodiment may bebased on the above-described method 2 in relation to a structure of thePN pilot.

According to an embodiment, the pre-configured pilot signal may betransmitted in a specific time domain. The pre-configured pilot signalmay be positioned before the time domain allocated for the cyclic prefix(CP) of the PTRS. The embodiment may be based on the above-describedmethod 1 in relation to the structure of the PN pilot.

According to the above-described S2610, an operation of a wirelessdevice (100/200 of FIGS. 27 to 31 ) which receives the pre-configuredpilot signal and the phase tracking reference signal (PTRS) from anotherwireless device (100/200 of FIGS. 27 to 31 ) may be implemented by theapparatuses of FIGS. 27 to 31 . For example, referring to FIG. 28 , oneor more processors 102/202 may control one or more transceivers 106/206and/or one or more memories 104/204 to receive the pre-configured pilotsignal and the phase tracking reference signals from another wirelessdevice 200/100.

In S2620, the wireless device (UE or BS) calculates the first phasenoise based on the pre-configured pilot signal.

According to an embodiment, the first phase noise may be based on a meanof the phase noise during a time duration for which the pre-configuredpilot signal is transmitted. The first phase noise may be a valuecalculated in the time domain based on the pre-configured pilot signal(PN pilot described above). Specifically, the first phase noise may bebased on PN_(est)(q) of Equation 3 above.

According to the above-described S2620, an operation which the wirelessdevice (100/200 of FIGS. 27 to 31 ) which calculates the first phasenoise based on the pre-configured pilot signal may be implemented by theapparatuses of FIGS. 27 to 31 . For example, referring to FIG. 28 , oneor more processors 102/202 may control one or more transceivers 106/206and/or one or more memories 104/204 so as to calculate the first phasenoise based on the pre-configured pilot signal.

In S2630, the wireless device (UE or BS) calculates a common phase error(CPE) based on the phase tracking reference signal (PTRS).

According to an embodiment, the common phase error (CPE) may be a valuecalculated in a frequency domain based on the phase tracking referencesignal (PTRS). That is, the common phase error (CPE) may be based onPN_(PTRS)(q) of Equation 4 above.

According to the above-described S2630, an operation which the wirelessdevice (100/200 of FIGS. 27 to 31 ) which calculates the common phaseerror (CPE) based on the phase tracking reference signal (PTRS) may beimplemented by the apparatuses of FIGS. 27 to 31 . For example,referring to FIG. 28 , one or more processors 102/202 may control one ormore transceivers 106/206 and/or one or more memories 104/204 so as tocalculate the common phase error (CPE) based on the phase trackingreference signal (PTRS).

In S2640, the wireless device (UE or BS) estimates phase noise (PN)based on the first phase noise and the common phase error (CPE).

According to an embodiment, the PN may be estimated throughinterpolation based on a specific reference point. The specificreference point may be based on the first phase noise and the CPE.

According to an embodiment, at least one time function related to the PNmay be determined based on the specific reference point and the firstphase noise. A mean of integral values based on the at least one timefunction may be equal to the CPE. The embodiment may be based onEquation 5 above. In this case, the at least one time function mayinclude at least one of PN1t(t) or PN2(t). The specific reference pointmay mean a new reference point of the interpolation.

According to an embodiment, the specific reference point may satisfy thefollowing equation.

PN _(m)(q)=2CPE _(PTRS)(q)−½PN _(est)(q)−½PN _(est)(q+1)

Here, q may represent the symbol index, PN_(m) may represent thespecific reference point, CPE_(PTRS) may represent the CPE, and PN_(est)may represent the first phase noise.

According to the above-described S2640, an operation which the wirelessdevice (100/200 of FIGS. 27 to 31 ) which estimates a phase noise (PN)based on the first phase noise and the common phase error (CPE) may beimplemented by the apparatuses of FIGS. 27 to 31 . For example,referring to FIG. 28 , one or more processors 102/202 may control one ormore transceivers 106/206 and/or one or more memories 104/204 so as toestimate the phase noise (PN) based on the first phase noise and thecommon phase error (CPE).

Example of Communication System Applied to Present Disclosure

The various descriptions, functions, procedures, proposals, methods,and/or operational flowcharts of the present disclosure described inthis document may be applied to, without being limited to, a variety offields requiring wireless communication/connection (e.g., 6G) betweendevices.

Hereinafter, a description will be certain in more detail with referenceto the drawings. In the following drawings/description, the samereference symbols may denote the same or corresponding hardware blocks,software blocks, or functional blocks unless described otherwise.

FIG. 27 illustrates a communication system 1 applied to the presentdisclosure.

Referring to FIG. 27 , a communication system 1 applied to the presentdisclosure includes wireless devices, Base Stations (BSs), and anetwork. Herein, the wireless devices represent devices performingcommunication using Radio Access Technology (RAT) (e.g., 5G New RAT(NR)) or Long-Term Evolution (LTE)) and may be referred to ascommunication/radio/5G devices. The wireless devices may include,without being limited to, a robot 100 a, vehicles 100 b-1 and 100 b-2,an eXtended Reality (XR) device 100 c, a hand-held device 100 d, a homeappliance 100 e, an Internet of Things (IoT) device 100 f, and anArtificial Intelligence (AI) device/server 400. For example, thevehicles may include a vehicle having a wireless communication function,an autonomous driving vehicle, and a vehicle capable of performingcommunication between vehicles. Herein, the vehicles may include anUnmanned Aerial Vehicle (UAV) (e.g., a drone). The XR device may includean Augmented Reality (AR)/Virtual Reality (VR)/Mixed Reality (MR) deviceand may be implemented in the form of a Head-Mounted Device (HMD), aHead-Up Display (HUD) mounted in a vehicle, a television, a smartphone,a computer, a wearable device, a home appliance device, a digitalsignage, a vehicle, a robot, etc. The hand-held device may include asmartphone, a smartpad, a wearable device (e.g., a smartwatch or asmartglasses), and a computer (e.g., a notebook). The home appliance mayinclude a TV, a refrigerator, and a washing machine. The IoT device mayinclude a sensor and a smartmeter. For example, the BSs and the networkmay be implemented as wireless devices and a specific wireless device200 a may operate as a BS/network node with respect to other wirelessdevices.

The wireless devices 100 a to 100 f may be connected to the network 300via the BSs 200. An AI technology may be applied to the wireless devices100 a to 100 f and the wireless devices 100 a to 100 f may be connectedto the AI server 400 via the network 300. The network 300 may beconfigured using a 3G network, a 4G (e.g., LTE) network, or a 5G (e.g.,NR) network. Although the wireless devices 100 a to 100 f maycommunicate with each other through the BSs 200/network 300, thewireless devices 100 a to 100 f may perform direct communication (e.g.,sidelink communication) with each other without passing through theBSs/network. For example, the vehicles 100 b-1 and 100 b-2 may performdirect communication (e.g. Vehicle-to-Vehicle(V2V)/Vehicle-to-everything (V2X) communication). The IoT device (e.g.,a sensor) may perform direct communication with other IoT devices (e.g.,sensors) or other wireless devices 100 a to 100 f.

Wireless communication/connections 150 a, 150 b, or 150 c may beestablished between the wireless devices 100 a to 100 f/BS 200, or BS200/BS 200. Herein, the wireless communication/connections may beestablished through various RATs (e.g., 5G NR) such as uplink/downlinkcommunication 150 a, sidelink communication 150 b (or, D2Dcommunication), or inter BS communication (e.g. relay, Integrated AccessBackhaul (IAB)). The wireless devices and the BSs/the wireless devicesmay transmit/receive radio signals to/from each other through thewireless communication/connections 150 a and 150 b. For example, thewireless communication/connections 150 a and 150 b may transmit/receivesignals through various physical channels. To this end, at least a partof various configuration information configuring processes, varioussignal processing processes (e.g., channel encoding/decoding,modulation/demodulation, and resource mapping/demapping), and resourceallocating processes, for transmitting/receiving radio signals, may beperformed based on the various proposals of the present disclosure.

Example of Wireless Devices Applied to Present Disclosure

FIG. 28 illustrates wireless devices applicable to the presentdisclosure.

Referring to FIG. 28 , a first wireless device 100 and a second wirelessdevice 200 may transmit radio signals through a variety of RATs (e.g.,LTE and NR). Herein, {the first wireless device 100 and the secondwireless device 200} may correspond to {the wireless device 100 x andthe BS 200} and/or {the wireless device 100 x and the wireless device100 x} of FIG. 27 .

The first wireless device 100 may include one or more processors 102 andone or more memories 104 and additionally further include one or moretransceivers 106 and/or one or more antennas 108. The processor(s) 102may control the memory(s) 104 and/or the transceiver(s) 106 and may beconfigured to implement the descriptions, functions, procedures,proposals, methods, and/or operational flowcharts disclosed in thisdocument. For example, the processor(s) 102 may process informationwithin the memory(s) 104 to generate first information/signals and thentransmit radio signals including the first information/signals throughthe transceiver(s) 106. The processor(s) 102 may receive radio signalsincluding second information/signals through the transceiver 106 andthen store information obtained by processing the secondinformation/signals in the memory(s) 104. The memory(s) 104 may beconnected to the processor(s) 102 and may store a variety of informationrelated to operations of the processor(s) 102. For example, thememory(s) 104 may store software code including commands for performinga part or the entirety of processes controlled by the processor(s) 102or for performing the descriptions, functions, procedures, proposals,methods, and/or operational flowcharts disclosed in this document.Herein, the processor(s) 102 and the memory(s) 104 may be a part of acommunication modem/circuit/chip designed to implement RAT (e.g., LTE orNR). The transceiver(s) 106 may be connected to the processor(s) 102 andtransmit and/or receive radio signals through one or more antennas 108.Each of the transceiver(s) 106 may include a transmitter and/or areceiver. The transceiver(s) 106 may be interchangeably used with RadioFrequency (RF) unit(s). In the present disclosure, the wireless devicemay represent a communication modem/circuit/chip.

The second wireless device 200 may include one or more processors 202and one or more memories 204 and additionally further include one ormore transceivers 206 and/or one or more antennas 208. The processor(s)202 may control the memory(s) 204 and/or the transceiver(s) 206 and maybe configured to implement the descriptions, functions, procedures,proposals, methods, and/or operational flowcharts disclosed in thisdocument. For example, the processor(s) 202 may process informationwithin the memory(s) 204 to generate third information/signals and thentransmit radio signals including the third information/signals throughthe transceiver(s) 206. The processor(s) 202 may receive radio signalsincluding fourth information/signals through the transceiver(s) 106 andthen store information obtained by processing the fourthinformation/signals in the memory(s) 204. The memory(s) 204 may beconnected to the processor(s) 202 and may store a variety of informationrelated to operations of the processor(s) 202. For example, thememory(s) 204 may store software code including commands for performinga part or the entirety of processes controlled by the processor(s) 202or for performing the descriptions, functions, procedures, proposals,methods, and/or operational flowcharts disclosed in this document.Herein, the processor(s) 202 and the memory(s) 204 may be a part of acommunication modem/circuit/chip designed to implement RAT (e.g., LTE orNR). The transceiver(s) 206 may be connected to the processor(s) 202 andtransmit and/or receive radio signals through one or more antennas 208.Each of the transceiver(s) 206 may include a transmitter and/or areceiver. The transceiver(s) 206 may be interchangeably used with RFunit(s). In the present disclosure, the wireless device may represent acommunication modem/circuit/chip.

Hereinafter, hardware elements of the wireless devices 100 and 200 willbe described more specifically. One or more protocol layers may beimplemented by, without being limited to, one or more processors 102 and202. For example, the one or more processors 102 and 202 may implementone or more layers (e.g., functional layers such as PHY, MAC, RLC, PDCP,RRC, and SDAP). The one or more processors 102 and 202 may generate oneor more Protocol Data Units (PDUs) and/or one or more Service Data Unit(SDUs) according to the descriptions, functions, procedures, proposals,methods, and/or operational flowcharts disclosed in this document. Theone or more processors 102 and 202 may generate messages, controlinformation, data, or information according to the descriptions,functions, procedures, proposals, methods, and/or operational flowchartsdisclosed in this document. The one or more processors 102 and 202 maygenerate signals (e.g., baseband signals) including PDUs, SDUs,messages, control information, data, or information according to thedescriptions, functions, procedures, proposals, methods, and/oroperational flowcharts disclosed in this document and provide thegenerated signals to the one or more transceivers 106 and 206. The oneor more processors 102 and 202 may receive the signals (e.g., basebandsignals) from the one or more transceivers 106 and 206 and acquire thePDUs, SDUs, messages, control information, data, or informationaccording to the descriptions, functions, procedures, proposals,methods, and/or operational flowcharts disclosed in this document.

The one or more processors 102 and 202 may be referred to ascontrollers, microcontrollers, microprocessors, or microcomputers. Theone or more processors 102 and 202 may be implemented by hardware,firmware, software, or a combination thereof. As an example, one or moreApplication Specific Integrated Circuits (ASICs), one or more DigitalSignal Processors (DSPs), one or more Digital Signal Processing Devices(DSPDs), one or more Programmable Logic Devices (PLDs), or one or moreField Programmable Gate Arrays (FPGAs) may be included in the one ormore processors 102 and 202. The descriptions, functions, procedures,proposals, methods, and/or operational flowcharts disclosed in thisdocument may be implemented using firmware or software and the firmwareor software may be configured to include the modules, procedures, orfunctions. Firmware or software configured to perform the descriptions,functions, procedures, proposals, methods, and/or operational flowchartsdisclosed in this document may be included in the one or more processors102 and 202 or stored in the one or more memories 104 and 204 so as tobe driven by the one or more processors 102 and 202. The descriptions,functions, procedures, proposals, methods, and/or operational flowchartsdisclosed in this document may be implemented using firmware or softwarein the form of code, commands, and/or a set of commands.

The one or more memories 104 and 204 may be connected to the one or moreprocessors 102 and 202 and store various types of data, signals,messages, information, programs, code, instructions, and/or commands.The one or more memories 104 and 204 may be configured by Read-OnlyMemories (ROMs), Random Access Memories (RAMs), Electrically ErasableProgrammable Read-Only Memories (EPROMs), flash memories, hard drives,registers, cash memories, computer-readable storage media, and/orcombinations thereof. The one or more memories 104 and 204 may belocated at the interior and/or exterior of the one or more processors102 and 202. The one or more memories 104 and 204 may be connected tothe one or more processors 102 and 202 through various technologies suchas wired or wireless connection.

The one or more transceivers 106 and 206 may transmit user data, controlinformation, and/or radio signals/channels, mentioned in the methodsand/or operational flowcharts of this document, to one or more otherdevices. The one or more transceivers 106 and 206 may receive user data,control information, and/or radio signals/channels, mentioned in thedescriptions, functions, procedures, proposals, methods, and/oroperational flowcharts disclosed in this document, from one or moreother devices. For example, the one or more transceivers 106 and 206 maybe connected to the one or more processors 102 and 202 and transmit andreceive radio signals. For example, the one or more processors 102 and202 may perform control so that the one or more transceivers 106 and 206may transmit user data, control information, or radio signals to one ormore other devices. The one or more processors 102 and 202 may performcontrol so that the one or more transceivers 106 and 206 may receiveuser data, control information, or radio signals from one or more otherdevices. The one or more transceivers 106 and 206 may be connected tothe one or more antennas 108 and 208 and the one or more transceivers106 and 206 may be configured to transmit and receive user data, controlinformation, and/or radio signals/channels, mentioned in thedescriptions, functions, procedures, proposals, methods, and/oroperational flowcharts disclosed in this document, through the one ormore antennas 108 and 208. In this document, the one or more antennasmay be a plurality of physical antennas or a plurality of logicalantennas (e.g., antenna ports). The one or more transceivers 106 and 206may convert received radio signals/channels etc. from RF band signalsinto baseband signals in order to process received user data, controlinformation, radio signals/channels, etc. using the one or moreprocessors 102 and 202. The one or more transceivers 106 and 206 mayconvert the user data, control information, radio signals/channels, etc.processed using the one or more processors 102 and 202 from the baseband signals into the RF band signals. To this end, the one or moretransceivers 106 and 206 may include (analog) oscillators and/orfilters.

Example of a Signal Process Circuit for a Transmission Signal Applied toPresent Disclosure

FIG. 29 illustrates a signal process circuit for a transmission signalapplied to the present disclosure.

Referring to FIG. 29 , a signal processing circuit 1000 may includescramblers 1010, modulators 1020, a layer mapper 1030, a precoder 1040,resource mappers 1050, and signal generators 1060. An operation/functionof FIG. 29 may be performed, without being limited to, the processors102 and 202 and/or the transceivers 106 and 206 of FIG. 28 . Hardwareelements of FIG. 29 may be implemented by the processors 102 and 202and/or the transceivers 106 and 206 of FIG. 28 . For example, blocks1010 to 1060 may be implemented by the processors 102 and 202 of FIG. 28. Alternatively, the blocks 1010 to 1050 may be implemented by theprocessors 102 and 202 of FIG. 28 and the block 1060 may be implementedby the transceivers 106 and 206 of FIG. 28 .

Codewords may be converted into radio signals via the signal processingcircuit 1000 of FIG. 29 . Herein, the codewords are encoded bitsequences of information blocks. The information blocks may includetransport blocks (e.g., a UL-SCH transport block, a DL-SCH transportblock). The radio signals may be transmitted through various physicalchannels (e.g., a PUSCH and a PDSCH).

Specifically, the codewords may be converted into scrambled bitsequences by the scramblers 1010. Scramble sequences used for scramblingmay be generated based on an initialization value, and theinitialization value may include ID information of a wireless device.The scrambled bit sequences may be modulated to modulation symbolsequences by the modulators 1020. A modulation scheme may includepi/2-Binary Phase Shift Keying (pi/2-BPSK), m-Phase Shift Keying(m-PSK), and m-Quadrature Amplitude Modulation (m-QAM). Complexmodulation symbol sequences may be mapped to one or more transportlayers by the layer mapper 1030. Modulation symbols of each transportlayer may be mapped (precoded) to corresponding antenna port(s) by theprecoder 1040. Outputs z of the precoder 1040 may be obtained bymultiplying outputs y of the layer mapper 1030 by an N*M precodingmatrix W. Herein, N is the number of antenna ports and M is the numberof transport layers. The precoder 1040 may perform precoding afterperforming transform precoding (e.g., DFT) for complex modulationsymbols. Alternatively, the precoder 1040 may perform precoding withoutperforming transform precoding.

The resource mappers 1050 may map modulation symbols of each antennaport to time-frequency resources. The time-frequency resources mayinclude a plurality of symbols (e.g., a CP-OFDMA symbols and DFT-s-OFDMAsymbols) in the time domain and a plurality of subcarriers in thefrequency domain. The signal generators 1060 may generate radio signalsfrom the mapped modulation symbols and the generated radio signals maybe transmitted to other devices through each antenna. For this purpose,the signal generators 1060 may include Inverse Fast Fourier Transform(IFFT) modules, Cyclic Prefix (CP) inserters, Digital-to-AnalogConverters (DACs), and frequency up-converters.

Signal processing procedures for a signal received in the wirelessdevice may be configured in a reverse manner of the signal processingprocedures 1010 to 1060 of FIG. 29 . For example, the wireless devices(e.g., 100 and 200 of FIG. 28 ) may receive radio signals from theexterior through the antenna ports/transceivers. The received radiosignals may be converted into baseband signals through signal restorers.To this end, the signal restorers may include frequency downlinkconverters, Analog-to-Digital Converters (ADCs), CP remover, and FastFourier Transform (FFT) modules. Next, the baseband signals may berestored to codewords through a resource demapping procedure, apostcoding procedure, a demodulation processor, and a descramblingprocedure. The codewords may be restored to original information blocksthrough decoding. Therefore, a signal processing circuit (notillustrated) for a reception signal may include signal restorers,resource demappers, a postcoder, demodulators, descramblers, anddecoders.

Example of Application of a Wireless Device Applied to PresentDisclosure

FIG. 30 illustrates another example of a wireless device applied to thepresent disclosure. The wireless device may be implemented in variousforms according to a use-case/service (refer to FIG. 27 ).

Referring to FIG. 30 , wireless devices 100 and 200 may correspond tothe wireless devices 100 and 200 of FIG. 28 and may be configured byvarious elements, components, units/portions, and/or modules. Forexample, each of the wireless devices 100 and 200 may include acommunication unit 110, a control unit 120, a memory unit 130, andadditional components 140. The communication unit may include acommunication circuit 112 and transceiver(s) 114. For example, thecommunication circuit 112 may include the one or more processors 102 and202 and/or the one or more memories 104 and 204 of FIG. 28 . Forexample, the transceiver(s) 114 may include the one or more transceivers106 and 206 and/or the one or more antennas 108 and 208 of FIG. 28 . Thecontrol unit 120 is electrically connected to the communication unit110, the memory 130, and the additional components 140 and controlsoverall operation of the wireless devices. For example, the control unit120 may control an electric/mechanical operation of the wireless devicebased on programs/code/commands/information stored in the memory unit130. The control unit 120 may transmit the information stored in thememory unit 130 to the exterior (e.g., other communication devices) viathe communication unit 110 through a wireless/wired interface or store,in the memory unit 130, information received through the wireless/wiredinterface from the exterior (e.g., other communication devices) via thecommunication unit 110.

The additional components 140 may be variously configured according totypes of wireless devices. For example, the additional components 140may include at least one of a power unit/battery, input/output (I/O)unit, a driving unit, and a computing unit. The wireless device may beimplemented in the form of, without being limited to, the robot (100 aof FIG. 27 ), the vehicles (100 b-1 and 100 b-2 of FIG. 27 ), the XRdevice (100 c of FIG. 27 ), the hand-held device (100 d of FIG. 27 ),the home appliance (100 e of FIG. 27 ), the IoT device (100 f of FIG. 27), a digital broadcast terminal, a hologram device, a public safetydevice, an MTC device, a medicine device, a fintech device (or a financedevice), a security device, a climate/environment device, the AIserver/device (400 of FIG. 27 ), the BSs (200 of FIG. 27 ), a networknode, etc. The wireless device may be used in a mobile or fixed placeaccording to a use-example/service.

In FIG. 30 , the entirety of the various elements, components,units/portions, and/or modules in the wireless devices 100 and 200 maybe connected to each other through a wired interface or at least a partthereof may be wirelessly connected through the communication unit 110.For example, in each of the wireless devices 100 and 200, the controlunit 120 and the communication unit 110 may be connected by wire and thecontrol unit 120 and first units (e.g., 130 and 140) may be wirelesslyconnected through the communication unit 110. Each element, component,unit/portion, and/or module within the wireless devices 100 and 200 mayfurther include one or more elements. For example, the control unit 120may be configured by a set of one or more processors. As an example, thecontrol unit 120 may be configured by a set of a communication controlprocessor, an application processor, an Electronic Control Unit (ECU), agraphical processing unit, and a memory control processor. As anotherexample, the memory 130 may be configured by a Random Access Memory(RAM), a Dynamic RAM (DRAM), a Read Only Memory (ROM)), a flash memory,a volatile memory, a non-volatile memory, and/or a combination thereof.

Example of a Hand-Held Device Applied to Present Disclosure

FIG. 31 illustrates a hand-held device applied to the presentdisclosure. The hand-held device may include a smartphone, a smartpad, awearable device (e.g., a smartwatch or a smartglasses), or a portablecomputer (e.g., a notebook). The hand-held device may be referred to asa mobile station (MS), a user terminal (UT), a Mobile Subscriber Station(MSS), a Subscriber Station (SS), an Advanced Mobile Station (AMS), or aWireless Terminal (WT).

Referring to FIG. 31 , a hand-held device 100 may include an antennaunit 108, a communication unit 110, a control unit 120, a memory unit130, a power supply unit 140 a, an interface unit 140 b, and an I/O unit140 c. The antenna unit 108 may be configured as a part of thecommunication unit 110. Blocks 110 to 130/140 a to 140 c correspond tothe blocks 110 to 130/140 of FIG. 30 , respectively.

The communication unit 110 may transmit and receive signals (e.g., dataand control signals) to and from other wireless devices or BSs. Thecontrol unit 120 may perform various operations by controllingconstituent elements of the hand-held device 100. The control unit 120may include an Application Processor (AP). The memory unit 130 may storedata/parameters/programs/code/commands needed to drive the hand-helddevice 100. The memory unit 130 may store input/output data/information.The power supply unit 140 a may supply power to the hand-held device 100and include a wired/wireless charging circuit, a battery, etc. Theinterface unit 140 b may support connection of the hand-held device 100to other external devices. The interface unit 140 b may include variousports (e.g., an audio I/O port and a video I/O port) for connection withexternal devices. The I/O unit 140 c may input or output videoinformation/signals, audio information/signals, data, and/or informationinput by a user. The I/O unit 140 c may include a camera, a microphone,a user input unit, a display unit 140 d, a speaker, and/or a hapticmodule.

As an example, in the case of data communication, the I/O unit 140 c mayacquire information/signals (e.g., touch, text, voice, images, or video)input by a user and the acquired information/signals may be stored inthe memory unit 130. The communication unit 110 may convert theinformation/signals stored in the memory into radio signals and transmitthe converted radio signals to other wireless devices directly or to aBS. The communication unit 110 may receive radio signals from otherwireless devices or the BS and then restore the received radio signalsinto original information/signals. The restored information/signals maybe stored in the memory unit 130 and may be output as various types(e.g., text, voice, images, video, or haptic) through the I/O unit 140c.

Effects of the method and the apparatus for estimating the phase noisein the wireless according to an embodiment of the present disclosure aredescribed below.

Since the common phase error (CPE) cannot be used as a valuerepresenting a PN value at a specific time, the CPE causes many errorsas a reference point for interpolation.

According to an embodiment of the present disclosure, a first phasenoise is calculated in a time region from a preset pilot signal, and thecommon phase error (CPE) is calculated from a phase tracking referencesignal (PTRS) and phase noise (PN) is estimated through interpolationbased on a specific reference point. The specific reference point isbased on the first phase noise and the CPE. Therefore, interpolation forestimating phase noise is performed based on the specific referencepoint that can indicate a PN value at a specific time, so thatestimation performance can be further improved.

In order to determine the specific reference point, the preconfiguredpilot signal must be transmitted in addition to the phase trackingreference signal (PTRS). According to an embodiment of the presentdisclosure, the preconfigured pilot signal is transmitted in one regionof a time region allocated for a cyclic prefix (CP) of the PTRS, and theone region is positioned at a frontmost portion of the time regionallocated for the CP. Therefore, it is possible to minimize InterSymbolInterference (ISI) and system change caused by adding a new pilot to anexisting PTRS pilot.

In the aforementioned embodiments, the elements and characteristics ofthe present disclosure have been combined in a specific form. Each ofthe elements or characteristics may be considered to be optional unlessotherwise described explicitly.

Each of the elements or characteristics may be implemented in a form tobe not combined with other elements or characteristics. Furthermore,some of the elements or the characteristics may be combined to form anembodiment of the present disclosure. The sequence of the operationsdescribed in the embodiments of the present disclosure may be changed.Some of the elements or characteristics of an embodiment may be includedin another embodiment or may be replaced with corresponding elements orcharacteristics of another embodiment. It is evident that an embodimentmay be constructed by combining claims not having an explicit citationrelation in the claims or may be included as a new claim by amendmentsafter filing an application.

The embodiment according to the present disclosure may be implemented byvarious means, for example, hardware, firmware, software or acombination of them. In the case of an implementation by hardware, theembodiment of the present disclosure may be implemented using one ormore application-specific integrated circuits (ASICs), digital signalprocessors (DSPs), digital signal processing devices (DSPDs),programmable logic devices (PLDs), field programmable gate arrays(FPGAs), processors, controllers, microcontrollers, microprocessors,etc.

In the case of an implementation by firmware or software, the embodimentof the present disclosure may be implemented in the form of a module,procedure or function for performing the aforementioned functions oroperations. Software code may be stored in the memory and driven by theprocessor. The memory may be located inside or outside the processor andmay exchange data with the processor through a variety of known means.

It is evident to those skilled in the art that the present disclosuremay be materialized in other specific forms without departing from theessential characteristics of the present disclosure. Accordingly, thedetailed description should not be construed as being limitative fromall aspects, but should be construed as being illustrative. The scope ofthe present disclosure should be determined by reasonable analysis ofthe attached claims, and all changes within the equivalent range of thepresent disclosure are included in the scope of the present disclosure.

1-15. (canceled)
 16. A method performed by a user equipment (UE) in awireless communication system, the method comprising: receiving, from abase station, signal related to a cell search, wherein the signalrelated to the cell search includes Primary Synchronization Signal(PSS), Secondary Synchronization Signal (SSS) and Physical BroadcastChannel (PBCH); transmitting, to the base station, a preamble for arandom access procedure; receiving, from the base station, a responsefor the preamble; obtaining, from the base station, configurationinformation related to a plurality of reference signals (RSs),receiving, from the base station, a first RS; receiving, from the basestation, a second RS; estimating a first phase noise (PN) based on thefirst RS; estimating a second PN based on the second RS; and estimatinga third PN based on the first PN and the second PN, wherein the third PNis estimated through interpolation based on a specific reference point,and wherein the specific reference point is based on the first PN andthe second PN.
 17. The method of claim 16, wherein the second RS is aphase tracking reference signal (PTRS) and the first RS is transmittedin a time region allocated for a cyclic prefix (CP) of the PTRS.
 18. Themethod of claim 17, wherein the first RS is transmitted in a frontmostportion of the time region allocated for the CP.
 19. The method of claim16, wherein the second RS is a phase tracking reference signal (PTRS)and the first RS is transmitted in a specific time region and thespecific time region is positioned before the time region allocated forthe CP of the PTRS.
 20. The method of claim 16, wherein the first PN isbased on an mean of phase noise during a time duration for which thefirst RS is transmitted and the second PN is a common phase error (CPE).21. The method of claim 20, wherein at least one time function relatedto the third PN is determined based on the specific reference point andthe first PN, and wherein a mean of integral values based on the atleast one time function is equal to the CPE.
 22. The method of claim 21,wherein the specific reference point satisfies the following equation,

PN

_m(q)=

2CPE

_PTRS(q)−½

PN

_est(q)−½

PN

_est(q+1) wherein q represents a symbol index,

PN

_m represents the specific reference point,

CPE

_PTRS represents the CPE, and

PN

_est represents the first PN.
 23. A user equipment (UE) operating in awireless communication system, the UE comprising: one or moretransceivers; one or more processors controlling the one or moretransceivers; and one or more memories operatively connectable to theone or more processors, and storing instructions of performingoperations when the estimation of the phase noise is executed by the oneor more processors, wherein the operations include receiving, from abase station, signal related to a cell search, wherein the signalrelated to the cell search includes Primary Synchronization Signal(PSS), Secondary Synchronization Signal (SSS) and Physical BroadcastChannel (PBCH); transmitting, to the base station, a preamble for arandom access procedure; receiving, from the base station, a responsefor the preamble; obtaining, from the base station, configurationinformation related to a plurality of reference signals (RSs),receiving, from the base station, a first RS; receiving, from the basestation, a second RS, estimating a first phase noise (PN) based on thefirst RS, estimating a second PN based on the second RS, and estimatinga third PN based on the first PN and the second PN, wherein the third PNis estimated through interpolation based on a specific reference point,and wherein the specific reference point is based on the first PN andthe second PN.
 24. The UE of claim 23, wherein the second RS is a phasetracking reference signal (PTRS) and the first RS is transmitted in atime region allocated for a cyclic prefix (CP) of the PTRS.
 25. The UEof claim 23, wherein the second RS is a phase tracking reference signal(PTRS) and the first RS is transmitted in a specific time region and thespecific time region is positioned before the time region allocated forthe CP of the PTRS.
 26. The UE of claim 23, wherein the first PN isbased on an mean of the phase noise during a time duration for which thefirst RS is transmitted and the second PN is a common phase error (CPE).27. The UE of claim 26, wherein at least one time function related tothe third PN is determined based on the specific reference point and thefirst PN, and wherein a mean of integral values based on the at leastone time function is equal to the CPE.
 28. The UE of claim 27, whereinthe specific reference point satisfies the following equation,

PN

_m(q)=

2CPE

_PTRS(q)−½

PN

_est(q)−½

PN

_est(q+1) wherein q represents a symbol index,

PN

_m represents the specific reference point,

CPE

_PTRS represents the CPE, and

PN

_est represents the first PN.
 29. One or more non-transitorycomputer-readable media storing one or more instructions, wherein one ormore instructions executable by one or more processors are configured toinstruct an apparatus to receive, from a base station, signal related toa cell search, wherein the signal related to the cell search includesPrimary Synchronization Signal (PSS), Secondary Synchronization Signal(SSS) and Physical Broadcast Channel (PBCH); transmit, to the basestation, a preamble for a random access procedure; receive, from thebase station, a response for the preamble; obtain, from the basestation, configuration information related to a plurality of referencesignals (RSs), receive, from the base station, a first RS; receive, fromthe base station, a second RS, estimate a first phase noise (PN) basedon the first RS, estimate a second PN based on the second RS, andestimate a third PN based on the first PN and the second PN, wherein thethird PN is estimated through interpolation based on a specificreference point, and wherein the specific reference point is based onthe first PN and the second PN.